#j2k codec

[divulgaoeste]

Pins de vídeo no Pinterest

Pins de vídeo no pinterest, A diversão começou, é assim que o pinterest está anunciando para os usuários da plataforma, sobre a nova funcionalidade de posta vídeo. Uma ótima maneira de abranger um post sobre sua marca, produto ou serviço, pode ser colocado uma imagem de capa , que é ótimo para chamar mais a atenção do público em geral. Os vídeos serão reproduzidos no feed dos usuários, pois assim que aparece seu vídeo no feed ele inicia automático. Para os anunciantes também poderá criar vídeos de anúncios. Vale lembrar que esta função é apenas em contas business.

Pins de vídeo no Pinterest

Como Criar um Pin de vídeo no Pinterest

Entre na sua conta business e clique em  ou em Criar Pin na página inicialClique em  e carregue um vídeo para salvar como PinDeslize o seletor para a esquerda ou a direita da imagem no vídeo que você gostaria de usar como foto da capaDê ao Pin um título e uma descrição e adicione-o a uma pasta usando o menu suspenso em "Escolha uma pasta"Insira um site ao qual vincular o vídeo ou imagemClique em Publicar Se você quiser carregar vários vídeos também pode é só clicar em  + para criar pins de vídeo adicionais. lembrando que pode carregar até 2 GB de vídeo de uma vez.  Se você pensou que para criar pins de vídeo no Pinterest é simples e pronto já foi! nãoo, o Pinterest irá analisar seus vídeos e se estiver dentro das diretrizes da comunidade,  dentro de 24 horas seu vídeo estará postando.

Como fazer Upload em massa de Pins de vídeo

Agora, se você quer upar vários vídeos de uma única vez também pode, basta apenas usar a aba de criar pins em massa no pinterest nas configurações. Neste caso pode se carregar até 200 videos mais será apenas para criadores de vídeos selecionado. Está função é diferente da função de postar vários videos.

Qual tipo de vídeo e formato pode ser enviado no pinterest?

Está é uma pergunta bem comum, as especificações são do padrão de formato e largura máxima. DescriçãoFormatoTipo de arquivo.mp4, .mov ou. m4vTamanho do arquivoMáximo de 2 GBCodificaçãoH.264 ou H.265Duração4 segundos a 15 minutosDuração recomendada para anúncios de vídeo: 6 a 15 segundosTextoTítulo: até 100 caracteres. Se não houver título, a descrição será mostrada no feed.Descrição: até 500 caracteres.Para o título ou a descrição, os primeiros 50 a 60 caracteres provavelmente aparecem no feed.Taxa de proporçãoExigido para vídeo padrão: mais alto do que 1.91:1 e menor que 1:2Recomendado para vídeo padrão: 1:1 (tela quadrada) ou 2:3, 4:5 ou 9:16 (tela vertical)Exigido para vídeo de largura máxima: 1:1 (tela quadrada) ou 16.9 (widescreen)Pins de vídeo no Pinterest Codecs de áudio e vídeo Codecs de vídeoCodecs de áudioNenhum contêinerDV/DVCPROAVC (H.264)HEVC (H.265)MPEG-1MPEG-2PCMAudio Video Interleave (AVI) Não compactadoCanopus HQDivX/XvidDV/DVCPRO Dolby Digital (AC3)Dolby Digital Plus (EAC3)Quadros Dolby E em streams PCMMP3Áudio MPEGPCMAdobe Flash AVC (H.264)Arquivo Flash 9H.263 AACMatroska AVC (H.264)PCMMPEG-2MPEG-4 parte 2VC-1 AACDolby Digital (AC3)Dolby Digital Plus (EAC3)WMAWMA2IMF Apple ProResJPEG 2000 (J2K)PCMStreams de transporte MPEG AVC (H.264)HEVC (H.265)MPEG-2VC-1 AACAIFFDolby Digital (AC3)Dolby Digital Plus (EAC3)Quadros Dolby E em streams PCMÁudio MPEGPCMWMAWMA2Streams de sistema MPEG-1 MPEG-1MPEG-2 AACAIFFDolby Digital (AC3)Dolby Digital (EAC3)MPEGÁudio PCMMPEG-4 Não compactadoAVC Intra 50/100DivX/XvidH.261H.262H.263AVC (H.264)HEVC (H.265)JPEG 2000MJPEGMPEG-2MPEG-4 parte 2VC-1 AACDolby Digital (AC3)Dolby Digital Plus (EAC3)PCMWMAWMA2MXF Não compactadoApple ProResAVC Intra 50/100DNxHDDV/DVCPRODV25DV50DVCPro HDAVC (H.264)JPEG 2000 (J2K)MPEG-2Panasonic P2SonyXDCamProxy MPEG-4 Sony XDCam AACAIFFQuadros Dolby E em streams PCMÁudio MPEGPCMQuickTime Não compactadoApple ProResAVC Intra 50/100DivX/XvidDV/DVCPROH.261H.262H.263AVC (H.264)HEVC (H.265)JPEG 2000 (J2K)MJPEGMPEG-2MPEG-4 parte 2QuickTime Animation (RLE) AACMP3PCMWebM VP8VP9 VorbisWMV/ASF VC-1 WMAWMA2Pins de vídeo no Pinterest Por enquanto a respeito de Pins de vídeo no Pinterest é isso, está ferramenta será muito útil para anunciar, espero que tenha gostado, então compartilhe nosso post e deixe seu comentário. até a próxima =D Read the full article

[nskilop]

Nitro pdf professional 6.1.2.1

#Nitro pdf professional 6.1.2.1 movie

DVD, Video CD (VCD), Super Video CD (SVCD) BMP, CLP, CUR, EPS, FAX, FPX, GIF, ICO, IFF, IMG, J2K, JP2, JPC, JPG, PCD, PCT, PCX, PIC, PNG, PSD, PSPImage, PXR, RAS, RAW, SCT, SHG, TGA, TIF, UFO, UFP, WMF Dolby Digital Stereo, Dolby Digital 5.1, MP3, MPA, WAV, QuickTime, Windows Media Audio AVI, MPEG-one, MPEG-2, AVCHD, MPEG-4, H.264, BDMV, DV, HDV, DivX, QuickTime, RealVideo, Windows Media, MOD (file format JVC MOD), M2TS, M2T, TOD, 3GPP, 3GPP2

#Nitro pdf professional 6.1.2.1 movie

* Burn CDs, CDs DVD, Blu-ray and AVCHD, HD-video recording on a standard DVD-media for playback in DVD player or Blu-ray.Ĭorel VideoStudio Pro Package now includes a program DVD Movie Factory Pro 2010 - integrated the possibility of creating and recording on DVD and Blu-ray. * File Creation HD MPEG-4 codec using H.264. * Customizable sound tracks from SmartSound surround and Dolby Digital 5.1. * Transfer Movies device iPod, iPhone, PSP and other mobile devices. * Import video in high definition format from any source, including CDs HDV, AVCHD, Blu-ray Disc camcorders and JVC HD. With headlines in the Hollywood-style transitions, panning, zooming, and many more can be quickly create video clips from the finished film. * DVD authoring and Blu-ray disc, including menus BD-J. * Convert video, video footage and photos in animated videos and drawings made by hand a few clicks. * Package Effects NewBlue, including the effects of three-dimensional motion. * Accelerating through preview effects in real time. * Create movies in minutes and the subsequent fine-tuning with additional editing tools. * Acceleration of the GPU and NVIDIA CUDA optimization for the CPU Intel Core i7. * Complete the processing of video - import, editing, recording and sharing video in a format standard or high definition. The program provides high-performance video processing and integrated support for high-definition format. Includes a rich set of features, professional templates, effects, studio-quality, ready titles and transitions. Quote: Corel VideoStudio Pro X3 13.6.2.42 | 1.1 GBĬorel VideoStudio Pro X3 integrated program, authoring and burning a DVD and Blu-ray discs. Download Corel VideoStudio Pro X3 13.6.2.42 crackCorel VideoStudio Pro X3 13.6.2.42 rapidshare

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Motion Jpeg Codec Mac Download

Jpeg Codec Download

Motion Jpeg Codec Mac Download Full

Motion Jpeg Codec Mac Download Full

Motion Jpeg Codec For Mac

Jul 25, 2021 Download logitech quickcam webcam driver 10. Refer to below picture to modify the video CODEC to JPEG(MJPEG) 2. Mar 31, 2020 Simply put, ProRes RAW is a codec created by Apple to encode RAW. Download over 392 raw footage royalty free Stock Footage Clips, Motion. Mar 18, 2015 — Then. Download Morgan Multimedia Motion JPEG Codec for free. Morgan Multimedia Motion JPEG Codec - Morgan M-JPEG codec is a multimedia compressor/decompressor which registers into the Windows collection of multimedia drivers and integrates with any application using DirectShow and Microsoft Video for Windows.

Quick Solutions (for playback): Mac OS: Virtually any version of QuickTime Player should be able to play Motion JPEG. Windows: 1. Download DirectX 8 or above from Microsoft* DirectX 9.0c November 2008 (Windows XP) DirectX 9.0c August 2008 (Windows 2000) DirectX 9.0c October 2006 (Windows 98, Me) 2. Play the file in QuickTime Player Current version of QuickTime Player (Windows XP, Vista) QuickTime 7.1.6 (Windows 2000) QuickTime 6.5.2 (Windows 98, Me) QuickTime 5.0.2 (Windows 95) 3. Play the file in VirtualDub http://www.virtualdub.org 4. Buy it from one of the companies that sell them. MainConcept Morgan Multimedia Pegasus Imaging LEAD Multimedia Prices range from 10 to 30 dollars (not budget busters) and most have free trial periods. (There may be others out there like PMatrix (Paradigm Matrix) or Matrox.) * Windows NT 4.0’s latest supported version of DirectX is 3.0, and Microsoft’s free Motion JPEG decoder did not appear until version 8, so users of Windows NT 4.0 will have to use one of the other three solutions. More Info: Motion JPEG is very commonly used in digital camera movies. The basic idea is every frame of the movie is a JPEG image; this is easy to implement since one does not need to worry about interframe compression (compression is done independently of the previous and the upcoming frame). Microsoft’s free Motion JPEG decoder included with DirectX is DirectShow-only, so decoding in a VfW (Video for Windows) utility (like for example Adobe Premiere) will not work. (VirtualDub is an exception: although it’s also VfW, it has its own internal MJPEG decoder, so that’s why it works.) In addition, Microsoft’s codec is decode-only, and some individuals have stated its performance doesn’t match that of commercial offerings (takes a lot of CPU power and may drop frames/play choppy on slower processors). If performance is an issue, or you need to decode MJPEG in a VfW utility, or you want to encode in MJPEG, getting one of the commercial MJPEG codecs may be the better choice. Keywords: MJPG, mjpg, Motion JPEG, MJPEG, M-JPEG

Jpeg Codec Download

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JPEG 2000 Compressor v.1.0Using JPEG2000 Compressor you can easily convert pictures of JPEG and BMP format to JPEG2000 format with the quality and color characteristics specified by you. Besides you can change the picture order. The results can be saved on your computer.

Mpeg-8 Jpeg video codec v.1.0Codec Mpeg-8 De alta definicion para archivos avi , la resolucion maxima del Mpeg-8 es de 3840x2160 y la tasa de transferencia es de 120 Mbp/s sin audio.

J2K-Codec v.2.1.0 Build 18181Make your graphics look better with an easy-to-use JPEG2000 decoding component. J2K-Codec can help game developers, screensaver creators and many others to improve their products - to achieve better image quality and reduce installation package ...

OpenJPEG v.1.5The OpenJPEG library is an open-source JPEG2000codec written in C language.

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Batch It! Ultra v.3.9896Batch It! Ultra is an automated Batch Imaging Editor which allows you to resize, rename, rotate, slice, create thumbnail galleries, add captions.Supports 22 image formats include JPG, JPEG2000, GIF,PNG, BMP, TIFF, PCX, PSP, PSD and more ...

IRedSoft Image Resizer v.5.15iRedSoft Image Resizer is a windows-based program which resize and converts JPG, BMP, PNG, JPEG2000 and GIF images. Does the work fast and easy. Supports Ratio Aspects, retain EXIF/IPTC header, resize by pixel dimensions or ratio and add shadow effect. ...

Batch J2K Converter v.1.1Batch J2K Converter is a blazing fast batch image converter which is made to handle large jpeg2000 images fast. It allows you to resize, convert, add watermark, add caption and more to your images. Apart from JPEG2000, it can handle JPEG, BMP, TIFF ...

A-PDF Image to PDF v.2.2.0A-PDF Image to PDF is an utility that lets you convert photos, drawings, scans and faxes into Acrobat PDF documents. It supports almost all of image formats includes TIFF, JPEG, GIF, BMP, PNG and ICO etc, even acquiring images from scanner directly.

VISCOM TIFF to PDF OCR OCX SDK ActiveX v.5.01Powerful image activex ocx, Loading, printing and save image files that include PDF,BMP, GIF, ICO, JP2, JPC, JPEG, PCX, PDF,PGX, PNG, PNM, RAS. Convert multipage TIF to multipage PDF. Read, write EXIF ,TIFF tags. Split, Merge Swap TIFF.

AVS Image Converter v.4.1.1.285Convert and save tons of images between such formats as JPEG, PDF, RAW, TIFF, etc. Use Batch mode to speed up the conversion process. Apply correction settings to improve images quality. Select among various effects and watermark converted images.

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Virtual Image Printer Driver Pro v.4.0.0.0ImagePrinter Pro is installed as a virtual printer driver that allows you to convert printable documents from any application which supports printing into a standard BMP, GIF, JPEG, JPEG2000, PDF, TIFF,PNG image , DjVu, SWF o RTF doc.Image ...

VISCOM Image SDK ActiveX OCX v.7.09Image viewer CP is a simple and easy ActiveX OCX control to creates,crop, displays, edits, flips, resizes and rotates image, photo and graphic. It supports BMP, GIF, ICO, JPEG, JPEG2000, PCX, PNG, PSD, TIFF, WMF, WBMP, TGA, PGX, RAS, PNM, and others ...

VISCOM Image Viewer CP Pro ActiveX SDK v.11.06Powerful image processing activex, TIFF PDF to docx sdk activex, Loading, printing and save image files that include PDF,BMP, GIF, ICO, JP2, JPC, JPEG, PCX, PDF,PGX, PNG, PNM, RAS. Convert multipage TIF to multipage PDF.Merge Swap TIFF.

Batch It! v.4.90Batch It! is an automated Batch Imaging Editor which allows you to resize, rename , add captions, add drop shadow, print and enhance images.

Batch TIFF Resizer v.3.16Batch TIFF Resizer is a specialized tool which converts, resize, add caption, reorder, extract TIFF, PDF, PNG, JPEG, JPEG2000 and BMP and vice versa. It works with single and multipage TIFF. Comes in native 32 Bit and 64 Bit versions.

A-PDF Image Extractor v.1.0.0A-PDF Image Extractor is a simple, lightning-fast desktop utility program that lets you extract images files from Acrobat PDF files. It is able to be able to process a batch of PDF files one time and save the output image files to various formats.

Image Viewer CP Gold ActiveX PDF/A v.2.03Powerful image activex ocx, support output PDF/A and read Kodak / Wang Annotation tags of TIFF file , PDF. Read, write EXIF ,TIFF tags. Split, Merge Swap TIFF.

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Jpeg 2000 Codec software by TitlePopularityFreewareLinuxMac

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VISCOM Scanner TWAIN Docx PDF SDK Capture images from all the TWAIN compliant scanner and

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Motion Jpeg Codec For Mac

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The Broadcast Bridge (26/02/2019)

Video Compression is About to Get Faster, Higher, and Stronger

… JPEG2000 (J2K): By far the most widely used wavelet-based codec. The standardized filename extension is .jp2 for ISO/IEC 15444-1 compliant files and .jpx for the extended part-2 specifications, published as ISO/IEC 15444-2. Offers a compression ratio of up to 10:1.

[softrls-blog]

Zoner Photo Studio X 19.1809.2.83

Zoner Photo Studio helps you take control of your photos. Zoner Photo Studio is a complete toolbox for managing and processing digital photos. Acquire pictures from your camera, organize your archive, and edit and share your photos – it’s never been easier! Zoner Photo Studio is useful for beginners, advanced users, and experienced photographers, who can select one out of four different variants. Zoner Photo Studio is made for every user of digital cameras, scanners, and mobile devices. It contains everything you need for quality digital photo processing from start to finish: tools for downloading to your computer, for automatic and manual editing and defect correction in pictures, for easy photo management, and for sharing and publishing photos. Zoner Photo Studio also provides effects from fun (e.g. oil painting and posterization) to highly professional (e.g. framing and text-in-image). • Do all your photo jobs in one place: downloading, editing, basic and advanced edits, and sharing. • Get to work fast thanks to tools like the first run wizard and to the intuitive interface. • Get impressive results already just by learning a few easy edits. • Save lots of time thanks to automatic photo sorting, batch edits, and direct integration with Zonerama online galleries. Bitmap formats: JPEG, GIF, TIFF, PNG, PSD, PSB, PCD, BMP, PCX, TGA, ICO, RLE, MAC, WPG, DIB, BMI, PSP, PSPIMAGE, THM, HDP, WDP, JP2, J2K, JPC, PNM, PPM, PAM, PBM, PGM, WBMP, BMS, JPS, PNS, DCM, KDC, MPO, JXR, HDR RAW formats: CRW, CR2, DNG, MRW, NEF, ORF, PEF, ARW, SRF, SR2, MEF, ERF, RAW, RAF, FFF, RWZ, RWL, 3FR, CS1, SRW Expand the range of read-supported formats by installing extra WIC codecs (e.g. WebP). Video formats: ASF, AVI, M1V, MOV, MP4, MPE, MPEG, MPG, MTS, OGV, QT, WMV Install MPlayer (third-party freeware) to add support for these video formats: 3GP, DIVX, DV, DVR-MS, FLV, M2T, M2TS, M2V, M4V, MKV, MPV, MQV, NSV, OGG, OGM, RAM, REC, RM, RMVB, TS, VCD, VFW, VOB Zoner Photo Studio Highlights: • Optimized and faster RAW module • Catalog, advanced picture management and archival • Multi-exposures help you to do the “impossible” • Transparency support • Healing Brush and Selection Brush tool • Direct upload to Facebook, Flickr, and Picassa Web Albums • 64-bit program version • A wide array of edits and effects for photo enthusiasts • HDR, panoramas, 3D photo maker • Convenient, time saving batch operations • Support for GPS map data • Easy and direct web publishing • Templates for calendars, contact lists, and more Take control of your digital photos with Zoner Photo Studio. Acquiring photos from your camera, your scanner, and even your screen has never been easier. Have fun organizing and browsing through your digital memories. With Zoner Photo Studio, you’ll be editing and personalizing your photos with special effects and even advanced functions like HDR processing. Wow your family and friends with 3D photos and impressive panoramas! Process RAW files conveniently and easily with an interface designed specially for photo enthusiasts. Whether you are sharing pictures with your family and friends or publishing to a web gallery, Zoner Photo Studio has the tools for you. Many options are available to help you organize and store your photos on external drives, DVDs, and other media. You’ll benefit from full support for SSE and MMX processor technologies and powerful multi-core processors. 48-bit color depth ensures you get the highest photo quality, and the color management support is outstanding! Download Zoner Photo Studio X 19.1809.2.83 Crack-rG (88 MB) https://rapidgator.net/file/5765b7a6d5eead16616a980a337492b5/Zoner_Photo_Studio_X_19.1809.2.83_softrls.com.rar.html http://turbobit.net/iamuyw5mthap.html Read the full article

[lorrainecparker]

CREATING YOUR OWN DCP’S – GETTING STARTED

One question that I get asked a lot is, “How do I create my own DCP’s for delivery to a Festival, or to theatres in general?”, and to be honest, it’s not an easy question to answer.  There is a lot of setup involved, no matter which NLE you’re using, so I thought the easiest way to go about doing this is to dedicate an article to the setup in each of the three main NLE’s, Media Composer, Premiere Pro and then FCPX, and then an article to the actual creation of the DCP itself.  In this article, we’re going to cover important things you need to keep in mind in general about DCP’s, their creation (no matter which NLE you use), and the delivery.  You’ll quickly see that the majority of your work will be done in your timeline, and you need to make sure you get everything right there, so you don’t run into problems down the line.

We’ve done a few articles here at PVC about creating DCP’s.  Rich Young did a write up back in 2013 about CuteDCP for Premiere Pro that you can check out here, and Don Starnes did a write up just the DCP creation process back in 2015, and you can check that out here.  We’re definitely at about that time where we need a refresher, as there are a lot of new options out there for everyone to use.

IMPORTANT THINGS TO GET OUT OF THE WAY NOW

This article is essentially about that.  There’s a bunch of important things that you need to know before you even start editing.  Heck, before you even start shooting.  Let’s start out with some DCP specs.

ASPECT RATIO’s & 2K vs 4K

Now, I’ve been making DCP’s for a long time now.  Probably a good 2-3 years, so I’ve created a ton of them.  What’s important to keep in mind is that in all the DCP’s that I’ve created, not one of them has been 4K.  Not one.  They are all 2K, either Flat or Scope.  As for the aspect ratios, you’re looking at 1998×1080 for Flat (just a little wider than HD, at 1.85:1 aspect, as opposed to HD that is 1.78:1), and then there’s Scope at 2048×858, at an aspect of 2.39:1.  You might be thinking “Who cares, I’ll worry about that when we’re done and ready to create the DCP!”.  WRONG.  You’ll worry about that before you even start shooting, because if you want a Scope aspect on your DCP, you might want to take that into consideration when everything is shot (camera type, format, matting on camera while shooting, etc).  The last thing that you want to have happen is that you’ve cut your masterpiece, only to realize that, in the end, you really wanted to have a Scope master, and all your footage is Flat, and you’re going to need to reposition all the footage, to get it to fit into the Scope aspect ratio.  Trust me, it’s a ton of work that can be saved by taking this into consideration in the end.  Remember, there’s nothing wrong with having a Flat master of your final production, just keep in mind that nine times out of ten it will be pillarboxed for the final product.

FRAME RATES

This is another big one that you need to consider.  DCP’s are 24 fps.  If you have a master that is 23.98 or (God forbid) 29.97, chances are you will need to convert before you make your DCP. Now, there is some good news in this.  There are a ton of productions out there shooting 23.98, and there are DCP creation programs, like CineAsset that will interpret 23.98 as 24, and let you create your DCP masters using your 23.98 masters, but this is something that’s exceptionally important to know before you get started, as the last thing you want is to get to the point where you’re ready to create your DCP, and the whole thing comes to a screeching halt, because the DCP creation program won’t accept your frame rate.

COLOR SPACE

Again, another very important concept to keep in mind when creating your DCP in your NLE.  You will need to do a color space conversion from REC709 (assuming you’re working in REC709) to DCE XYZ color space.  Now, I know that might sound confusing, but don’t worry, it’s actually a pretty simple process, as it will be a LUT that we’ll add to our footage, but that does then beg the question, where can you find the correct LUT, as there are a few XYZ color space LUT converters out there, and they all don’t look exactly the same.  There are other things to keep in mind as well, when it comes to the REC709 to XYZ color space conversion, and that is you will notice some luminance variations, and things might not look exactly the same in XYZ as it did in REC709, but don’t worry, we’ll take about that in the individual NLE’s themselves.

FORMAT

Here’s the big monkey wrench in all of this.  Most DCP creation software wants you to already have your footage in a “DCP Friendly” format, when you import it into the software, to create your DCP’s.  So, what codec are we looking to use for our DCP’s?  ProRes?  Nope.  Animation?   Negative!  We’re going to be working with JPEG 2000.  That’s right, now that does beg the question, where can we download this codec from, that we can work with it.  Believe it or not, the best codec that I’ve found is one that hasn’t been updated since 2012, and it’s fnord’s J2K plug-in.  It’s completely free, and the plug-in is for both Photoshop and After Effects.  Once you’ve added it to your plug-ins, it will come up in AE’s output module with the parameters looking a little like the below image.

Now, I want to also mention that there is a DaVinci Resolve workflow for creating DCP’s, but I’ll also dedicate an article to just that application, as I mostly use it as a JPEG 2000 conversion tool, and not so much as an editor!

QUALITY CONTROL

This is probably the most important part of the entire process, and one that you need to make sure you get right, as the last thing that you want is to be trying to deliver your final DCP to the theatre and they keep rejecting it due to something simple like the naming being incorrect, or the DCP not ingesting into their player.  You really, really need to make sure you have access to a DCP player, so you can properly QC your final DCP.  Checking it in the creation application is not enough.  Computer will play just about anything.  If your DCP isn’t created properly, the machine just won’t ingest, and chances are, won’t give you much of a reason why it won’t ingest, it just won’t do it.  This will require a little bit more money on your part, as you will need to have your DCP (depending on its size) on either a USB key or an external Hard Drive, to a facility that has a playout machine, but it’s worth the little bit of extra cost to do this, to avoid any hassles down the line.

Okay, I think that’s good enough to get started.  We’re going to cover everything you need to know, in depth, as we move along talking about the NLE’s and the actual DCP creation process itself!

Channel: www.youtube.com/letseditMC_avid Facebook: http://www.facebook.com/LetsEditwithMediaComposer Twitter: @kpmcauliffe e-mail: kevinpmcauliffe@gmail.com

The post CREATING YOUR OWN DCP’S – GETTING STARTED appeared first on ProVideo Coalition.

First Found At: CREATING YOUR OWN DCP’S – GETTING STARTED

[fastcompression]

Part 2: JPEG2000 solutions in science and healthcare. JP2 format limitations

Author: Fyodor Serzhenko

In the first part of the article, JPEG 2000 in science, healthcare, digital cinema and broadcasting, we discussed the key technologies of JPEG2000 and focused on its application in digital cinema.

In this second part, we will continue examining the functions of JPEG2000, as well as review its main drawback and talk about the other application areas where the format turned out to be in high demand. At the end we will present a solution which simplifies and makes the process of working with the format much more convenient.

1. JPEG2000 in science and medicine

Window mode support is one of the handy features that makes JPEG2000 attractive. Scientists often have to work with files of enormous resolution, the width and height of which can exceed 40,000 pixels, but only a small part of which is of interest. Standard JPEG would have to decode the entire image to work with it, while JPEG2000 allows you to decode only a selected area.

JP2 is also used for space photography. Those wonderful pictures of Mars taken, for example, with a HiRISE camera, are available in JP2 format. Still, the data link from space to Earth is subject to interference, so errors may occur during the transfer or even entire data packets may be lost. However, when the special mode is enabled, it is somewhat error-resilient, which can be helpful when communication or storage devices are unreliable. This mode allows you to detect errors that occur when data is lost during transmission. It is important to note that the image is divided into small blocks (for example, 32x32 or 64x64 pixels), where, after preliminary transformations, each bit plane is encoded separately. Thus, a lost bit most likely spoils only some of the less significant bit planes, and this usually has little effect on overall quality. By the way, in JPEG, the loss of a bit can lead to significant distortions of a big part of or even the entire image.

Regarding the operation of the special mode with the integrity check in the JPEG2000 format file, additional information is added to the compressed file to check the correctness of the data. Without this information, we often can’t determine during decoding whether there’s an error or not, and we continue the process as if nothing had happened. As a result, it’s still possible that even one erroneous bit will spoil quite a large part of the image. If this mode is enabled, however, then we detect any error when it appears and can limit its effect on other parts of the image.

The JPEG2000 format also plays important role in healthcare. In this application area, it is extremely important to maintain a sufficient bit depth of the source data to make it possible to fix all the subtleties of each area of the body under examination. JPEG2000 is used in CTs, X-rays, MRIs, etc.

Also, in accordance with FDA (Food and Drug Administration) requirements, images acquired by means of medical imaging must be stored in the original format (without loss). The JPEG2000 format is an ideal solution in this case.

Another interesting feature of JPEG2000 is the compression of three-dimensional data arrays. This can be highly relevant both in science and in medicine (for example, three-dimensional tomography results). The 10th part of the JPEG2000 standard is devoted to the compression of such data: JP3D (volumetric imaging).

2. JP2 format limitations

Unfortunately, JP2 (JPEG2000) isn’t so simple — in fact, it’s not supported by most web browsers (with the exception of Safari). The format is computationally complex, and existing open source codecs have been too slow for active use over the years. Even now, when the speed of processors is increasing with each new generation, and codecs are being optimized and accelerated, their capabilities still leave something to be desired. To illustrate the importance of codec speed, let's return to the topic of digital cinema for a moment: specifically, to the creation of DCPs (Digital Cinema Packages), the same set of files that we enjoy in cinemas. Again, JPEG2000 is the standard for digital cinema and, accordingly, is required to create a DCP package. Unfortunately, its computational complexity makes this task quite resource-intensive and time-consuming. Moreover, existing open source codecs don't allow decoding movies at the required rate of 25, 30 or 60 fps for 12-bit data at resolutions already in 2K or 4K.

3. How to speed up processing with the JP2 format

JPEG2000 provides modes for operating at a higher speed, but this is achieved at the expense of a slight reduction in quality or compression ratio. However, even the slightest reduction in image quality can be unacceptable for some application areas.

To speed up the process with JPEG2000, we at Fastvideo have developed our own implementation of the JPEG 2000 codec. Our solution is based on NVIDIA CUDA technology, thanks to which it’s now possible to make a parallel implementation of the coder and decoder using all CPU and GPU cores.

As a consequence, the Fastvideo solution performs much better in comparison to the competition and provides fundamentally new capabilities for users. We believe that our solution will encourage more people to use JP2 format, as well as significantly speed up JP2 processing for people who already use it. Our goal is to make high-quality images much more accessible for specialists in application areas where the original image quality is required by default (e.g., science and healthcare).

Other info from Fastvideo concerning JPEG 2000 solutions

JPEG2000 codec on GPU

JPEG2000 vs JPEG vs PNG: What's the Difference?

J2K encoding benchmarks

J2K decoding benchmarks

Fast FFmpeg J2K decoder on NVIDIA GPU

MXF Player

Original article see at: https://www.fastcompression.com/blog/jpeg2000-applications-part2.htm Subscribe to our mail list: https://mailchi.mp/fb5491a63dff/fastcompression

[fastcompression]

JPEG2000 in science, healthcare, digital cinema and broadcasting

Author: Fyodor Serzhenko

This article is devoted to the JPEG2000 algorithm and will be presented in two parts. In the first part, we will discuss the key technologies of the algorithm and explain why it has become so popular in digital cinema and broadcasting. In the second part, we will talk about other application areas and important features of JPEG2000. We will also discuss its main drawback and present a solution that can significantly improve the usability of JPEG2000.

Part 1: JPEG2000 in digital cinema and broadcasting. Features of JP2 format

The cinema captured the hearts and minds of people all over the world from the very beginning. Comedy movies by Charlie Chaplin and horror films by Alfred Hitchcock left no one indifferent. It took just a little bit more than a century for the industry to evolve from black-and-white silent cinema to IMAX movies, the quality of which leaves a deep impression the moment a spectator watches one for the first time.

Okay, but are you aware of what makes IMAX movies so captivating? And why does it differ so much in video quality from what we used to watch on standard TV channels? The answer is the compression algorithm and image format used.

JP2 is the file format for images compressed with the JPEG2000 algorithm

1. JPEG2000 in digital cinema

The JP2 format (among others) has been actively used in digital cinema for a long time. It was developed in 2000 and selected as a digital cinema standard by the Digital Cinema Initiatives (DCI) group, which includes Disney, Fox, Paramount, MGM, Sony Pictures Entertainment, Universal, and Warner Bros. Studios, in 2004. The same year, some amendments relating to digital cinema were added to the first part of the JPEG2000 standard.

Good compression for digital cinema was simply necessary. An hour-and-a-half movie in 2K or 4K resolution with 12-bit color channels and 24 fps, compressed using JPEG2000 at a standard bitrate of 250 Mbit/s takes up to 160 Gigabytes.

The JPEG2000 compression algorithm, thanks to which we can enjoy vivid images in IMAX, is based on two key technologies — a discrete wavelet transform (DWT) and embedded block coding with optimal truncation (EBCOT), each of which has its own role:

DWT creates a multi-scale image representation to select the spatial and frequency components of the image. It makes it possible, for example, to watch a 4K movie in 2K resolution.

EBCOT arranges the data about the pixels of each coded block by importance, providing a smooth degradation of the picture quality as the compression ratio increases.

2. Format features: 12-bit and lossless compression option

In this section, we will discuss the JPEG2000 format itself, its features and applications. So how come images in JP2 format are so fascinating? The answer is simple: the color depth. One of the most important advantages of the format is working with high-bit data. In other words, the JP2 format is designed to describe one pixel of an image using more bits than a monitor that is not designed for professional color work, and thereby store more information about color. If you compare a standard JPEG image (8 bits per channel) with images in the IMAX format (12 bits per channel), you’ll see that an 8-bit image simply cannot convey such a range of color and brightness as a 12-bit image. As a consequence, IMAX image quality differs fundamentally.

Another important advantage of the JPEG2000 algorithm is the relationship between the compression ratio and the image quality (measured by any metric). The image file size and the transmission speed depend on the compression ratio. What’s more, the quality of the restored image depends on the compression as well. It’s quite clear that the presence of artifacts does not delight anyone.

Thanks to the use of wavelets (DWT), images in JP2 don’t acquire such conspicuous artifacts at high compression ratios as in its predecessor JPEG — when compressing an image with JPEG, the boundaries of 8x8-pixel squares become visible. It’s impossible to completely avoid artifacts, but visually they’re much less noticeable. As a result, JPEG2000 allows you to compress images more, and lose much less quality than JPEG allows with the same compression ratios. You can find a more detailed comparison of JPEG2000 with JPEG in one of our articles.

It’s worth noting that JPEG2000 was developed to provide both lossy and mathematically lossless compression in a single compression architecture. Depending on its contents, an image can be compressed up to 2.5 times without any quality loss, while its data footprint is decreased to 60%. However, there are always exceptions: some images can’t be reduced in size using lossless compression or compression ratio would be close to 1, but it’s quite achievable for the majority of them. Anyway, such compression capabilities are in great demand wherever it’s necessary to store a large amount of data in a compressed form for a long time (e.g., documentation, images, and video), while maintaining the possibility of lossless recovery. For example, it can be quite useful in libraries, museums, etc.

Lossless compression is of great use in the following situations:

when advanced image analysis or multi-stage processing is performed or is supposed to be performed, and each stage can introduce an additional quality loss.

when minor details captured at the camera's sensitivity limit can be of great significance.

For example, early detection of diseases, research of nano-objects and processes at the sensitivity limit of a microscope, study of extremely distant space objects.

3. JPEG2000 in broadcasting

One more use case of the JPEG2000 format which is worth mentioning is sports broadcasting, such as football and basketball tournaments. During broadcasting, the still-uncompressed video is transmitted from the camera to an add-on device, which compresses the images using JPEG2000. Subsequently, they are transmitted in JP2 format to the server where the re-encoding is performed to create a video suitable for an audience. In this case, both fast image transmission and quality preservation are essential. JPEG2000 uses EBCOT coding, which makes it possible to select the order of alternation of resolutions, quality layers, color components and positions within compressed bytestream. Thanks to EBCOT coding, JPEG2000 supports dynamic quality distribution. In other words, it allows you to automatically adjust the amount of transmitted data depending on the bandwidth of the channel. Thus, images of the highest possible quality for a given IP channel are quickly transmitted to the servers.

To be continued…

Other info from Fastvideo concerning JPEG2000

JPEG2000 codec on GPU

JPEG2000 vs JPEG vs PNG: What's the Difference?

J2K encoding benchmarks

J2K decoding benchmarks

Fast FFmpeg J2K decoder on NVIDIA GPU

MXF Player

Remote color grading

Original article see at: https://www.fastcompression.com/blog/jpeg2000-applications-part1.htm Subscribe to our mail list: https://mailchi.mp/fb5491a63dff/fastcompression

[fastcompression]

Remote Color Grading and Editorial Review

When you are shooting a movie at tight schedule and need to accelerate your post production, then remote collaborative approach is a good choice. You don't need to have all professionals on-site because via remote approach you can collaborate with your teammates wherever they are located. Industry trend to remote solutions is quite clear and it happens not just due to the coronavirus. The idea to accelerate post production via remote operation is viable and companies tend to remove various limitations of conventional workflow - now the professionals could choose a place and a time to work remotely.

Nowadays, there are quite a lot of software solutions which could offer reliable remote access via local networks or via public internet. Most of them are built without an idea about professional usage at post production. Nevertheless, in color grading and editorial reviewing we need to utilize professional hardware which can visualize 10-bit and 12-bit frames. Most of existing video conference solutions (Skype, ZOOM, OBS) are not capable of doing that, so we've implemented a software to solve that task.

Remote color grading with existing hardware appliances

There are quite a lot of hardware units (encoding-decoding and IP streaming solutions) which together with software could offer high performance and low latency workflow to solve the task of remote color grading. These are fully-managed remote collaboration solutions for high-quality, realtime color grading, editing, digital intermediates and approvals:

Sohonet ClearView

Nevion Virtuoso

Streambox Chroma HD HDR, 4K HDR and DCI

Nimbra Media Gateway

VF-REC (Village Island)

These fast and quite expensive hardware appliances are not always available, especially if you are working from home. Below we present a software solution which is capable of running on conventional PC and be able to meet all requirements for remote color grading in terms of image quality, performance and latency.

How we do Remote Color Grading?

User has two screens: for the shared content and for video conferencing. The first screen is able to visualize 10/12-bit images to see the result of color grading, the other is necessary for access to remote PC, where color grading software is running.

We offer cost-effective software solution which is able to record, encode, transmit, receive, decode and visualize various transport streams and SDI signals. To ensure 24/7 operation with an ability to create and to process 2K live SDI streams with visually lossless encoding, we've applied the JPEG2000 (J2K) compression algorithm, which could be very fast on NVIDIA GPUs.

This is our basic workflow for remote color grading: Video Source (Baseband Video) -> Capture device (Blackmagic DeckLink or AJA Kona) -> SDI unpacking on GPU -> J2K Encoder on GPU -> Facility Firewall -> Public Internet -> Remote Firewall -> J2K Decoder on GPU -> SDI packing on GPU -> Output device (Blackmagic DeckLink or AJA Kona) -> Video Display (Baseband Video).

Here you can see more info for live workflow

Capture baseband video streams via HD-SDI or 3G-SDI frame grabber (Blackmagic DeckLink 8K Pro, AJA Kona 4 or Kona 5)

Live encoding with J2K codec that supports 10-bit YUV 4:2:2 and 8/10/12-bit 4:4:4 RGB

Send the encoded material to the receiver/decoder - point-to-point transmission over ethernet or public internet

Stream decoding - Rec.709/Rec.2020, 10-bit 4:2:2 YUV or 10/12-bit 4:4:4 RGB

Send stream to baseband video playout device (Blackmagic or AJA frame grabber) to display 10-bit YUV 4:2:2 or 8/10/12-bit 4:4:4 RGB material on external professional display

That basic workflow covers just the task of precise color visualization. Color grading is actually done via remote access to a PC with installed grading software. This is not difficult to do, though we need to be able to check image quality at remote professional monitor with high bit depth.

Values for Remote Color Grading

Reduce the cost of remote production

Cut down of travel and rent costs for the team

Low cost and high quality solution on conventional PC to work from home for videographers and editors

Your team will work on multiple projects (time saving and multi-tasking)

Remote work will allow to choose the best professionals to work with

Technical requirements

High speed acquisition and realtime processing of SD, HD and 3G-SDI streams

Input and output SDI formats: RGB, RGBA, v210, R10B, R10L, R12L

Fast JPEG2000 encoding and decoding (lossy or lossless) on NVIDIA GPU

High image quality

Color control and preview on professional monitor

Maximum possible bit depth (10-bit or 12-bit per channel)

Fast and reliable data transmission over internal or public network

Low latency

OS Linux Ubuntu/Debian/CentOS (Windows version is coming soon)

Recommended grabbers:

- Blackmagic 6G SDI: DeckLink Studio 4K, DeckLink SDI 4K

- Blackmagic 12G SDI: DeckLink 4K Extreme 12G, DeckLink 8K Pro

Recommended GPU: NVIDIA Quadro RTX 4000 / 5000 / 6000

J2K Streamer: j2k encoder - transmitter - receiver - j2k decoder

High performance implementation of lossless or lossy J2K algorithm

8-bit, 10-bit and 12-bit color depth

4:2:2 and 4:4:4 color subsampling

Color spaces Rec.709, DCI P3, Rec.2020

SD, HD, 3G 2K resolutions and frame rates (support for UHD and 4K is also available)

Security and content protection

AES 128-bit encryption with symmetric key both for video and audio

It's possible to encrypt both video and audio with 128-bit AES encryption with symmetric key without any increase in a stream latency. Please note that the encryption currently ensures only confidentiality. As a hash, CRC-32 is used, so it doesn't guarantee cryptographical integrity.

Low latency transport for realtime streaming

From 300 ms to 1 sec end-to-end latency

Public internet and/or fiber networking for remote sessions

10 to 250 Mbps bit rates

Maximum performance for JPEG2000 compression and decompression features could be achieved with multithreading at batch mode. This should be done to implement massive parallel processing according to J2K algorithm. At batch processing mode we need to collect several images, which is not good for end-to-end latency. Here we have a trade-off between performance and latency for the task of JPEG2000 encoding and decoding. For example, at remote color grading application we would be interested to have minimum latency, so we need to process each J2K frame separately, without batch. Though in most cases it's better to choose acceptable latency and get the best performance with batch and multithreading.

Performance measurements

Currently, our J2K encoder is faster than J2K decoder, so total performance is limited by J2K decoding. On NVIDIA Quadro RTX 6000 the software can offer 24 fps and more for 4K resolution at 12-bit with 4:4:4 subsampling. In the case with 2K resolution, the software could achieve more than 60 fps. The performance depends on GPU model and on parameters of J2K encoding, etc. We suggest to test network bandwidth and software latency to choose the best parameters.

Competitors

Our software is offering an approach for low-latency remote color grading. Please note that this is not actually the software for color grading. This is the solution to work remotely with conventional grading, VFX and post production software like Blackmagic Davinci Resolve, Adobe Premiere Pro, AVID Media Composer, Baselight, etc. We don't compete with these color grading applications at all.

We would recommend to utilize TeamViewer, AnyDesk, Google Remote Desktop, Ammyy Admin, Mikogo, ThinVNC, UltraVNC, WebEx Meetings, LogMeIn Pro, which could offer remote access to color grading software, but they are able to work with just 8-bit color frames instead of 12-bit color. This is the key difference. As soon as for high quality post production we need to work with 12-bit color, that requirement is essential and the task or low latency solution with acceptable compression ratio is very important. Still, any software from the above list is useful to ensure an access to remote PC.

Hardware-based solutions like Nevion, Streambox, Sohonet are our competitors as well. These are reliable and very expensive solutions. Our approach needs less hardware and could offer high quality, low latency and cheaper solution for remote color grading and post production.

Other info from Fastvideo about J2K and digital cinema applications

JPEG2000 codec on GPU

Fast FFmpeg J2K decoder on NVIDIA GPU

MXF Player

Fast CinemaDNG Processor software on GPU

BRAW Player and Converter for Windows and Linux

Original article see at: https://www.fastcompression.com/products/remote-color-grading.htm Subscribe to our mail list: https://mailchi.mp/fb5491a63dff/fastcompression

[fastcompression]

J2K codec performance on Jetson TX2

NVIDIA Jetson TX2 hardware is very promising for imaging and other embedded applications. That high-performance and low-power hardware is utilized in autonomous solutions, especially the industrial version Jetson TX2i. Since J2K compression is a common task for UAV (Unmanned Aerial Vehicle) applications, here we evaluate such a solution and its limitations.

Detailed info concerning our testing approach for JPEG2000 encoding and decoding on desktop/server NVIDIA GPUs you can find at the corresponding links. Here we follow exactly the same procedure, but it's applied to the Jetson hardware.

J2K encoding/decoding parameters

File format – JP2

Lossy JPEG2000 compression with CDF 9/7 wavelet

Lossless JPEG2000 compression with CDF 5/3 wavelet

Compression ratio (for lossy algorithm) ~ 12.0:1 which corresponds to visually lossless encoding

Subsampling mode – 4:4:4

Number of DWT resolutions – 7

Codeblock size – 32×32

MCT – on

PCRD – off

Tiling – off

Window – off

Quality layers – one

Progression order – LRCP (L = layer, R = resolution, C = component, P = position)

Modes of operation – single or multithreaded batch

2K test image (24-bit) – 2k_wild.ppm

4K test image (24-bit) – 4k_wild.ppm

It's obvious that in many cases compression ratio for visually lossless encoding could be much higher for JPEG2000 algorithm. So we would suggest testing different parameters to achieve the best compression ratio with an acceptable image quality. Decreasing the quality coefficient one can get not only better compression, but also higher framerate both for encoding and decoding. Our benchmarks show the performance results for the above images and parameters. It's not the maximum performance, which could be better in many other cases.

Hardware and software

NVIDIA Jetson TX2

CUDA Toolkit 10.2

JPEG2000 codec benchmarks on NVIDIA Jetson TX2

Jetson TX2 has 4-core ARM Cortex-A57 @ 2 GHz and 2-core Denver2 @ 2 GHz. These two types of cores have different performance, which should be taken into account. Since Tier-2 stage of JPEG2000 algorithm is implemented on CPU, the performance of both CPU and GPU cores determine the framerate. From that point of view, multithreading can be useful (we use up to 12 threads), but in the single mode we could get different performance depending on the CPU core used. So in the single mode we need to set affinity mask to ensure utilizing the fastest CPU core.

In the tests discussed we've restricted memory usage to 2 GB. This was done under an assumption that Jetson TX2 can have only 4 GB memory, so this is important limitation for the whole image processing solution.

Here we haven't considered the task of J2K transcoding to H.264 on Jetson. That task requires additional tests, though from our previous experience with desktop/server GPUs, performance of the transcoding should not differ significantly, because Jetson has hardware support of H.264 encoding (separate from GPU), which is accessible via V4L2 interface and can be used simultaneously with JPEG2000 decoder.

By request we could offer Fastvideo SDK for Jetson for evaluation - please fill the form below and send it to us.

Other info from Fastvideo concerning JPEG2000 and Jetson

JPEG2000 codec on GPU

JPEG2000 vs JPEG vs PNG: What's the Difference?

J2K encoding benchmarks

J2K decoding benchmarks

Fast FFmpeg J2K decoder on NVIDIA GPU

MXF Player

Jetson Benchmark Comparison: Nano vs TX2 vs Xavier

Jetson image processing for camera applications

Original article see at: https://www.fastcompression.com/blog/j2k-codec-on-jetson-tx2.htm

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[fastcompression]

Fastvideo SDK vs NVIDIA NPP Library

Author: Fyodor Serzhenko

Why is Fastvideo SDK better than NPP for camera applications?

What is Fastvideo SDK?

Fastvideo SDK is a set of software components which correspond to high quality image processing pipeline for camera applications. It covers all image processing stages starting from raw image acquisition from the camera to JPEG compression with storage to RAM or SSD. All image processing is done completely on GPU, which leads to real-time performance or even faster for the full pipeline. We can also offer a high-speed imaging SDK for non-camera applications on NVIDIA GPUs: offline raw processing, high performance web, digital cinema, video walls, FFmpeg codecs and filters, 3D, AR/VR, AI, etc.

Who are Fastvideo SDK customers?

Fastvideo SDK is compatible with Windows/Linux/ARM and is mostly intended for camera manufacturers and system integrators developing end-user solutions containing video cameras as a part of their products.

The other type of Fastvideo SDK customers are developers of new hardware or software solutions in various fields: digital cinema, machine vision and industrial, transcoding, broadcasting, medical, geospatial, 3D, AR/VR, AI, etc.

All the above customers need faster image processing with higher quality and better latency. In most cases CPU-based solutions are unable to meet such requirements, especially for multicamera systems.

Customer pain points

According to our experience and expertise, when developing end-user solutions, customers usually have to deal with the following obstacles.

Before starting to create a product, customers need to know the image processing performance, quality and latency for the final application.

Customers need reliable software which has already been tested and will not glitch when it is least expected.

Customers are looking for an answer on how to create a new solution with higher performance and better image quality.

Customers need external expertise in image processing, GPU software development and camera applications.

Customers have limited (time/human) resources to develop end-user solutions bound by contract conditions.

They need a ready-made prototype as a part of the solution to demonstrate a proof of concept to the end user.

They want immediate support and answers to their questions regarding the fast image processing software's performance, image quality and other technical details, which can be delivered only by industry experts with many years of experience.

Fastvideo SDK business benefits

Fastvideo SDK as a part of complex solutions allows customers to gain competitive advantages.

Customers are able to design solutions which earlier may have seemed to be impossible to develop within required timeframes and budgets.

The product helps to decrease the time to market of end-user solutions.

At the same time, it increases overall end-user satisfaction with reliable software and prompt support.

As a technology solution, Fastvideo SDK improves image quality and processing performance.

Fastvideo serves customers as a technology advisor in the field of fast image processing: the team of experts provides end-to-end service to customers. That means that all customer questions regarding Fastvideo SDK, as well as any other technical questions about fast image processing are answered in a timely manner.

Fastvideo SDK vs NVIDIA NPP comparison

NVIDIA NPP can be described as a general-purpose solution, because the company implemented a huge set of functions intended for applications in various industries, and the NPP solution mainly focuses on various image processing tasks. Moreover, NPP lacks consistency in feature delivery, as some specific image processing modules are not presented in the NPP library. This leads us to the conclusion that NPP is a good solution for basic camera applications only. It is just a set of functions which users can utilize to develop their own pipeline.

Fastvideo SDK, on the other hand, is designed to implement a full 16/32-bit image processing pipeline on GPU for camera applications (machine vision, scientific, digital cinema, etc). Our end-user applications are based on Fastvideo SDK, and we collect customer feedback to improve the SDK’s quality and performance. We are armed with profound knowledge of customer needs and offer an exceptionally reliable and heavily tested solution.

Fastvideo uses a specific approach in Fastvideo SDK which is based on components (not on functions as in NPP). It is easier to build a pipeline based on components, as the components' input and output are standardized. Every component executes a complete operation, and it can have a complex architecture, whereas NPP only uses several functions. It is important to emphasize here that developing an application using built-in Fastvideo SDK is much less complex than creating a solution based on NVIDIA NPP.

The Fastvideo JPEG codec and lots of other SDK features have been heavily tested by our customers for many years with a total performance benchmark of more than million images per second. This is a question of software reliability, and we consider it as one of our most important advantages.

The major part of the Fastvideo SDK components (debayer and codecs) can offer both high performance and image quality, leaving behind the NPP alternatives. What’s more, this is also true for embedded solutions on Jetson where computing performance is quite limited. For example, NVIDIA NPP only has a bilinear debayer, so it can be regarded as a low-quality solution, best suited only for software prototype development.

Summing up this section, we need to specify the following technological advantages of the Fastvideo SDK over NPP in terms of image processing modules for camera applications:

High-performance codecs: JPEG, JPEG2000 (lossless and lossy)

High-performance 12-bit JPEG encoder

Raw Bayer Codec

Flat-Field Correction together with dark frame subtraction

Dynamic bad pixel suppression in Bayer images

Four high quality demosaicing algorithms

Wavelet-based denoiser on GPU for Bayer and RGB images

Filters and codecs on GPU for FFmpeg

Other modules like color space and format conversions

To summarize, Fastvideo SDK offers an image processing workflow which is standard for digital cinema applications, and could be very useful for other imaging applications as well.

Why should customers consider Fastvideo SDK instead of NVIDIA NPP?

Fastvideo SDK provides better image quality and processing performance for implementing key algorithms for camera applications. The real-time mode is an essential requirement for any camera application, especially for multi-camera systems.

Over the last few years, we've tested NPP intensely and encountered software bugs which weren't fixed. In the meantime, if customers come to us with any bug in Fastvideo SDK, we fix it within a couple of days, because Fastvideo possesses all the source code and the image processing modules are implemented by the Fastvideo development team. Support is our priority: that's why our customers can rely on our SDK.

We offer custom development to meet specific our customers' requirements. Our development team can build GPU-based image processing modules from scratch according to the customer's request, whereas in contrast NVIDIA provides nothing of the kind.

We are focused on high-performance camera applications and we have years of experience, and our solutions have been heavily tested in many projects. For example, our customer vk.com has been processing 400,000 JPG images per second for years without any issue, which means our software is extremely reliable.

Software downloads to evaluate the Fastvideo SDK

GPU Camera Sample application with source codes including SDKs for Windows/Linux/ARM - https://github.com/fastvideo/gpu-camera-sample

Fast CinemaDNG Processor software for Windows and Linux - https://www.fastcinemadng.com/download/download.html

Demo applications (JPEG and J2K codecs, Resize, MG demosaic, MXF player, etc.) from https://www.fastcompression.com/download/download.htm

Fast JPEG2000 Codec on GPU for FFmpeg

You can test your RAW/DNG/MLV images with Fast CinemaDNG Processor software. To create your own camera application, please download the source codes from GitHub to get a ready solution ASAP.

Useful links for projects with the Fastvideo SDK

1. Software from Fastvideo for GPU-based CinemaDNG processing is 30-40 times faster than Adobe Camera Raw:

http://ir-ltd.net/introducing-the-aeon-motion-scanning-system

2. Fastvideo SDK offers high-performance processing and real-time encoding of camera streams with very high data rates:

https://www.fastcompression.com/blog/gpixel-gmax3265-image-sensor-processing.htm

3. GPU-based solutions from Fastvideo for machine vision cameras:

https://www.fastcompression.com/blog/gpu-software-machine-vision-cameras.htm

4. How to work with scientific cameras with 16-bit frames at high rates in real-time:

https://www.fastcompression.com/blog/hamamatsu-orca-gpu-image-processing.htm

Original article see at: https://www.fastcompression.com/blog/fastvideo-sdk-vs-nvidia-npp.htm Subscribe to our mail list: https://mailchi.mp/fb5491a63dff/fastcompression

[fastcompression]

Software for Hamamatsu ORCA Processing on GPU

Author: Fyodor Serzhenko

Scientific research demands modern cameras with low noise, high resolution, frame rate and bit depth. Such imaging solutions are indispensable in microscopy, experiments with cold atom gases, astronomy, photonics, etc. Apart from outstanding hardware there is a need for high performance software to process streams in realtime with high precision.

Hamamatsu Photonincs company is a world leader in scientific cameras, light sources, photo diodes and advanced imaging applications. For high performance scientific cameras and advanced imaging applications, Hamamatsu introduced ORCA cameras with outstanding features. ORCA cameras are high precision instruments for scientific imaging due to on-board FPGA processing enabling intelligent data reduction, pixel-level calibrations, increased USB 3.0 frame rates, purposeful and innovative triggering capabilities, patented lightsheet read out modes and individual camera noise characterization.

ORCA-Flash4.0 cameras have always provided the advantage of low camera noise. In quantitative applications, like single molecule imaging and super resolution microscopy imaging, fully understanding camera noise is also important. Every ORCA-Flash4.0 V3 is carefully calibrated to deliver outstanding linearity, especially at low light, to offer improved photo response non-uniformity (PRNU) and dark signal non-uniformity (DSNU), to minimize pixel differences and to reduce fixed pattern noise (FPN).

The ORCA-Flash4.0 V3 includes patented Lightsheet Readout Mode, which takes advantage of sCMOS rolling shutter readout to enhance the quality of lightsheet images. When paired with W-VIEW GEMINI image splitting optics, a single ORCA-Flash4.0 V3 camera becomes a powerful dual wavelength imaging device. In "W-VIEW Mode" each half of the image sensor can be exposed independently, facilitating balanced dual color imaging with a single camera. And this feature can be combined with the new and patented "Dual Lightsheet Mode" to offer simultaneous dual wavelength lightsheet microscopy.

Applications for Hamamatsu ORCA cameras

There are quite a lot of scientific imaging tasks which could be solved with Hamamatsu ORCA cameras:

Digital Microscopy

Light Sheet Fluorescence Microscopy

Live-Cell Microscopy and Live-Cell Imaging

Laser Scanning Confocal Microscopy

Biophysics and Biophotonics

Biological and Biomedical Sciences

Bioimaging and Biosensing

Neuroimaging

Hamamatsu ORCA-Flash4.0 V3 Digital CMOS camera (image from https://camera.hamamatsu.com/jp/en/product/search/C13440-20CU/index.html)

Hamamatsu ORCA-Flash4.0 V3 Digital CMOS camera: C13440-20CU

Image processing for Hamamatsu ORCA-Flash4.0 V3 Digital CMOS camera

That camera generates quite high data rate. Maximum performance for Hamamatsu ORCA-Flash4.0 V3 could be evaluated as 100 fps * 4 MPix * 2 Byte/Pix = 800 MByte/s. As soon as these are 16-bit monochrome frames, that high data rate could be a bottleneck to save such streams to SSD for two-camera system for long-term recording, which is quite usual for microscopy applications.

If we consider one-day recoding duration, storage for such a stream could be a problem. That two-camera system generates 5.76 TB data per hour and it could be a good idea to implement realtime compression to cut storage cost. To compress 16-bit frames, we can't utilize either JPEG or H.265 encoding algorithms because they don't support more than 12-bit data. The best choice here is JPEG2000 compression algorithm which is working natively with 16-bit images. On NVIDIA GeForce GTX 1080 we've got the performance around 240 fps for lossy JPEG2000 encoding with compression ratio around 20. This is the result that we can't achieve on CPU because corresponding JPEG2000 implementations (OpenJPEG, Jasper, J2K, Kakadu) are much slower. Here you can see JPEG2000 benchmark comparison for widespread J2K encoders.

JPEG2000 lossless compression algorithm is also available, but it offers much less compression ratio, usually in the range of 2-2.5 times. Still, it's useful option to store original compressed data without any losses which could be mandatory for particular image processing workflow. In any way, lossless compression makes data rate less, so it's always good for storage and performance issues.

Optimal compression ratio for lossy JPEG2000 encoding should be defined by checking different quality metrics and their correspondence to a particular task to be solved. Still, there is no good alternative for fast JPEG2000 compression for 16-bit data, so JPEG2000 looks as the best fit. We would also recommend to add the following image processing modules to the full pipeline to get better image quality:

Dynamic Bad Pixel Correction

Data linearization with 1D LUT

Dark Frame Subtraction

Flat Field Correction (vignette removal)

White/Black Points

Exposure Correction

Curves and Levels

Denoising

Crop, Flip/Flop, Rotate 90/180/270, Resize

Geometric transforms, Rotation to an arbitrary angle

Sharp

Gamma Correction

Realtime Histogram and Parade

Mapping and monitor output

Output JPEG2000 encoding (lossless or lossy)

The above image processing pipeline could be fully implemented on GPU to achieve realtime performance or even faster. It could be done with Fastvideo SDK and NVIDIA GPU. That SDK is supplied with sample applications in source codes, so user can create his own GPU-based application very fast. Fastvideo SDK is avalilable for Windows, Linux, L4T.

There is also a gpu-camera-sample application which is based on Fastvideo SDK. You can download source codes and/or binaries for Windows from the following link on Github - gpu camera sample. Binaries are able to work with raw images in PGM format (8/12/16-bit), even without a camera. User can add support for Hamamatsu cameras to process images in realtime on NVIDIA GPU.

Fastvideo SDK to process on GPU raw images from Hamamatsu ORCA sCMOS cameras

The performance of JPEG2000 codec strongly depends on GPU, image content, encoding parameters and complexity of the full image processing pipeline. To scale the performance, user can also utilize several GPUs for image processing at the same time. Multiple GPU processing option is the part of Fastvideo SDK.

If you have any questions, please fill the form below with your task description and send us your sample images for evaluation.

Links

Hamamatsu ORCA-Flash4.0 V3 Digital sCMOS camera

GPU Software for camera applications

JPEG2000 Codec on NVIDIA GPU

Image and Video Processing SDK for NVIDIA GPUs

GPU Software for machine vision and industrial cameras

Original article see at: https://www.fastcompression.com/blog/hamamatsu-orca-gpu-image-processing.htm

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[fastcompression]

Fast RAW Compression on GPU

Author: Fyodor Serzhenko

Recording performance for RAW data acquisition task is essential issue for 3D/4D, VR and Digital Cinema applications. Quite often we need to do realtime recordings to portable SSD and here we face questions about throughput, compression ratio, image quality, recording duration, etc. As soon as we need to store RAW data from a camera, the general approach for raw image encoding is not exactly the same as for color. Here we review several methods to solve that matter.

Why do we need Raw Image Compression on GPU?

We need to compress raw stream from a camera (industrial, machine vision, digital cinema, scientific, etc.) in realtime at high fps, for example 4K (12-bit raw data) at 60 fps, 90 fps or faster. This is vitally important issue for realtime applications, external raw recorders and for in-camera raw recordings. As an example we can consider RAW or RAW-SDI format to send data from a camera to PC or to external recorder.

As soon as most of modern cameras have 12-bit dynamic range, it's a good idea to utilize JPEG compression which could be implemented for 12-bit data. For 14-bit and 16-bit cameras this is not the case and for high bit depth cameras we would recommend to utilize either Lossless JPEG encoding or JPEG2000. These algorithms are not fast, but they can process high bit depth data.

Lossy methods to solve the task of Fast RAW Compression

Standard 12-bit JPEG encoding for grayscale images

Optimized 12-bit JPEG encoding (double width, half height, Standard 12-bit JPEG encoding for grayscale images)

Raw Bayer encoding (split RGGB pattern to 4 planes and then apply 12-bit JPEG encoding for each plane)

The problem with Standard JPEG for RAW encoding is evident - we don't have slowly varying changes in pixel values at the image and this could cause problems with image quality due to Discrete Cosine Transform which is the part of JPEG algorithm. In that case the main idea of JPEG compression is questionable and we expect to get higher level of distortion for RAW images with JPEG compression.

The idea about "double width" is also well-known. It's working well at Lossless JPEG compression for RAW bayer data. After such a transform we get the same colors for vertical pixel neighbours for two adjacent rows and it could decrease high-frequency values after DCT for Standard JPEG. That method is also utilized in Blackmagic Design BMD RAW 3:1 and 4:1 formats.

If we split RAW image into 4 planes according to available bayer pattern, we get 4 downsized images, one for each bayer component. Here we can get slowly varying intensity, but for images with halved resolution. That algorithm looks promising, though we could expect slightly slower performance becase of additional split algorithm in the pipeline.

We focus on JPEG-based methods as soon as we have high performance solution for JPEG codec on CUDA. That codec is capable of working with all range of NVIDIA GPUs: mobile Jetson Nano, TK1/TX1/TX2, AGX Xavier, laptop/desktop GeForce series and server GPUs Quadro and Tesla. That codec also supports 12-bit JPEG encoding which is the key algorithm for that RAW compression task.

There is also an opportunity to apply JPEG2000 encoding instead of JPEG for all three cases, but here we will consider JPEG only because of the following reasons:

JPEG encoding on GPU is much faster than JPEG2000 encoding (approximately ×20)

Compression ratio is almost the same (it's bigger for J2K, but not too much)

There is a patent from RED company to implement J2K encoding for splitted channels inside the camera

There are no open patent issues connected with JPEG algorithm and this is serious advantage of JPEG. Nevertheless, the case with JPEG2000 compression is very interesting and we will test it later. That approach could give us GPU lossless raw image compression, which can't be done with JPEG.

To solve the task of RAW image compression, we need to specify both metric and criteria to measure image quality losses. We will try SSIM which is considered to be much more reliable in comparison with PSNR and MSE. SSIM means structural similarity and it's widely used to evaluate image resemblance. This is well known image quality metric.

Quality and Compression Ratio measurements

To find the best solution among chosen algorithms we have done some tests to calculate Compression Ratio and SSIM for standard values of JPEG Quality Factor. We've utilized the same Standard JPEG quantization table and the same 12-bit RAW image. As soon as Compression Ratio is content-dependent, this is just an example of what we could get in terms of SSIM and Compression Ratio.

For the testing we've utilized uncompressed RAW bayer image from Blackmagic Design URSA camera with resolution 4032×2192, 12-bit. Compression Ratio was measured with relation to the packed uncompressed 12-bit image file size, which is equal to 12.6 MB, where two pixel values are stored in 3 Bytes.

Output RGB images were created with Fast CinemaDNG Processor software. Output colorspace was sRGB, 16-bit TIFF, no sharpening, no denoising. SSIM measurements were performed with these 16-bit TIFF images. Source image was compared with the processed image, which was encoded and decoded with each compression algorithm.

Table 1: Results for SSIM for encoding with standard JPEG quantization table

These results show that SSIM metrics is not really suitable for such tests. According to visual estimation, we can conclude that image quality Q = 80 and higher could be considered acceptable for all three algorithms, but the images from the third algorithm look better.

Table 2: Compression Ratio (CR) for encoding with standard JPEG quantization table

Performance for RAW encoding is the same for the first two methods, though for the third it's slightly less (performance drop is around 10-15%) because we need to spend additional time to split raw image to 4 planes according to the bayer pattern. Time measurements have been done with Fastvideo SDK for different NVIDIA GPUs. These are hardware-dependent results and you can do the same measurements for your particular NVIDIA hardware.

How to improve image quality, compression ratio and performance

There are several ways to get even better results in terms of image quality, CR and encoding performance for RAW compression:

Image sensor calibration

RAW image preprocessing: dark frame subtraction, bad pixel correction, white balance, LUT, denoise, etc.

Optimized quantization tables for 12-bit JPEG encoding

Optimized Huffman tables for each frame

Minimum metadata in JPEG images

Multithreading with CUDA Streams to get better performance

Better hardware from NVIDIA

Useful links converning GPU accelerated image compression

High Performance CUDA JPEG Codec

12-bit JPEG encoding on GPU

JPEG2000 Codec on GPU

RAW Bayer Codec on GPU

Lossless JPEG Codec on CPU

Original article see at: https://www.fastcompression.com/blog/fast-raw-compression.htm

[fastcompression]

Fastvideo SDK benchmarks on NVIDIA Quadro RTX 6000

Fastvideo SDK for Image and Video Processing on NVIDIA GPU offers super fast performance and high image quality. Now we've done testing of Fastvideo SDK on NVIDIA® Quadro RTX™ 6000 which is powered by the NVIDIA Turing™ architecture and NVIDIA RTX™ platform. That new technology brings the most significant advancement in computer graphics in over a decade to professional workflows. That new hardware is intended to boost the performance of image and video processing dramatically. To check that, we've done benchmarks for mostly frequently utilized image processing features.

We've done time measurements for most frequently used image processing algorithms like demosaic, resize, denoise, jpeg encoder and decoder, jpeg2000, etc. This is just a small part of Fastvideo SDK modules, though they could be valuable to understand the performance speedup on the new hardware.

To evaluate more complicated image processing pipelines we would suggest to download and to test Fast CinemaDNG Processor software which is based on Fastvideo SDK. With that software you will be able to create your own pipeline and to check the benchmarks for your images.

How we do benchmarking

As usual, performance benchmarks can just give an idea about the speed of processing, though exact values depend on OS, hardware, image content, resolution and bit depth, processing parameters, an approach of time measurements, etc. The origin of the particular image processing task could imply any specific type of benchmarking.

To get maximum performance for any GPU software, we need to ensure maximum GPU occupancy, which is not easy to accomplish. That's why we could evaluate max performance by the following ways:

Repetition for particular function to get averaged computation time

Multithreading with copy/compute overlap

Software profiling on NVIDIA Visual Profiler to get total GPU time for all kernels for particular image processing module

Hardware and software

CPU Intel Core i7-5930K (Haswell-E, 6 cores, 3.5–3.7 GHz)

GPU NVIDIA Quadro RTX 6000

OS Windows 10 (x64), version 1803

CUDA Toolkit 10

Fastvideo SDK 0.14.0

Demosaicing benchmarks

In the Fastvideo SDK we have three different GPU-based demosaicing algorithms at the moment:

HQLI - High Quality Linear Interpolation, window 5×5

DFPD - Directional Filtering and a Posteriori Decision, window 11×11

MG - Multiple Gradients, window 23×23

All these algorithms are implemented for 8-bit and 16-bit workflows, and they take into account pixels new image borders. To demonstrate the performance, we imply that initial and processed data reside in GPU memory. This is the case for complicated pipelines in raw image processing applications.

Demosaicing algorithm | 2K (1920 × 1080) | 4K (3840 × 2160)

HQLI (8-bit) | 30,000 fps | 9,300 fps

HQLI (16-bit) | 13,000 fps | 4,400 fps

DFPD (8-bit) | 12,600 fps | 4,700 fps

DFPD (16-bit) | 7,100 fps | 2,700 fps

MG (16-bit) | 3,400 fps | 1,200 fps

To check image quality for each demosaicing algorithm in real case, you can download Fast CinemaDNG Processor software from www.fastcinemadng.com together with sample DNG image series for evaluation.

JPEG encoding and decoding benchmarks

JPEG codec from Fastvideo SDK offers very high performance both for encoding and decoding. To get better results, we need to have more data to achieve maximum GPU occupancy. This is very important issue to get good results. Here we present results for the best total kernel time for JPEG encoding and decoding. JPEG compression quality q=90%, subsampling 4:2:0 (visually lossless compression), optimum number of restart markers.

JPEG Encoding | JPEG Decoding

2K (1920 × 1080) | 3,500 fps1,380 fps

4K (3840 × 2160) | 1,900 fps860 fps

5K (5320 × 3840) | 1,100 fps520 fps

JPEG2000 encoding benchmarks

We have high performance JPEG codec on GPU in the Fastvideo SDK and this is the algorithm which is partially utilizing CPU, so total performance is also CPU-dependent, but still it's much faster than any CPU-based J2K codecs like OpenJPEG. In the tests we utilized optimal number of threads, compression ratio corresponded to visually lossless compression.

JPEG2000 encoding parameters | Lossy encoding | Lossless encoding

2K image, 24-bit, cb 32×32 | 504 fps | 281 fps

4K image, 24-bit, cb 32×32 | 160 fps | 85 fps

8K image, 24-bit, cb 32×3 | 256 fps | 23 fps

Image resize

This is frequently utilized feature and here we present our results for GPU-based resize according to Lanczos algorithm.

"1/2 resolution" means 960 × 540 for 2K and 1920 × 1080 for 4K. "1 pixel" means 1919 × 1079 for 2K and 3839 × 2159 for 4K.

Resize BMP/PPM | 1/2 resolution | 1 pixel

2K image, 24-bit | 4,200 fps | 3,300 fps

4K image, 24-bit | 1,700 fps | 1,120 fps

Apart from that, we have done benchmarks for the following pipeline: jpeg decoding - resize - jpeg encoding, which is utilized in web applications.

Decode JPEG - Resize - Encode JPEG | 1/2 resolution | 1 pixel

2K jpg image, 24-bit | 996 fps | 845 fps

4K jpg image, 24-bit | 586 fps | 425 fps

To summarize, Fastvideo SDK benchmarks are quite fast, though we can see possibilities to make them better by further optimization of our CUDA kernels for Turing architecture.

Original article see at: https://www.fastcompression.com/blog/fastvideo-sdk-benchmarks-quadro-rtx-6000.htm

[fastcompression]

JPEG2000 vs JPEG vs PNG: What's the Difference?

JPEG2000 vs JPEG vs PNG

If you look for a list of image format standards with good compression ratio, a simple Google search will yield a lot of results. JPEG and the similar sounding JPEG2000, along with PNG, are among the best image compression formats today.

That being said, each of these formats has their particular strengths and weaknesses. For us to be able to distinguish one from another, we have to look at each one separately. Once we have described each of the three image formats, we will compare them together, so you can clearly see how they differ, and which is right for you.

There are other well-known raster image formats, which were not included in our comparison. GIF is actively used nowadays for animations, but it is limited by 256 color palettes. TIFF is a classical lossless format with support of extended precision (16 bits per channel), but it has weak compression and is not supported by most of the web browsers. There are also a number of newer formats, like JPEG XR, WebP and HEIF, which are not really popular due to very restricted support in web browsers and image processing software.

What is JPEG?

The acronym JPEG stands for Joint Photographic Expert Group (the name is derived from the company who made it). It first appeared on the stage in 1986 but is still the most popular imaging format today.

JPEG should not be confused with JPEG2000. These names are alike, because both standards were proposed by the same company, but they are completely different algorithms and formats; JPEG2000 is more recent and much more sophisticated one.

JPEG is originally lossy format, which means that encoding always causes loss of quality. The compression ratio can be significantly increased at the cost of more losses. It is the main feature, which made it so popular for compression of photographic images. They usually have smooth variations of brightness and color gradients allowing JPEG to achieve combination of good compression ratio with decent quality. However, the nature of JPEG algorithm causes appearance of blocking artifacts (especially noticeable near sharp edges with high contrast), which can be distractive at high compression ratios.

JPEG Features

The JPEG compression algorithm has several important features, which allowed it to gain impressive popularity:

Color space transformation allows to separate brightness (Y) and chrominance (Cb, Cr) components. Downscaling of Cb and Cr allows reducing file size with almost unnoticeable losses of quality.

Quantization after Discrete Cosine Transform allows to control reduction of image size by rounding coefficients for sharp (high-frequency) details.

Optional progressive encoding allows to show low-quality preview of the whole image after partial decoding of its byte stream.

Lossless entropy coding for DCT-transformed and quantized image data.

Pros and Cons of JPEG

When looked at as a whole, the features of JPEG make it a dependable format. Here are some of its advantages:

This format has been in use for quite a long time

Almost all devices can support JPEG, which is not the case for JPEG2000

It is compatible with most of the image processing apps

JPEG images can be compressed up to 5% of their initial size. It makes JPEG format more suitable one when it comes to transferring images over the Web

JPEG codec could be very fast on CPU and especially on GPU

Disadvantages of JPEG include:

Quality loss is inevitable after encoding and each iteration of import/ export

Due to ringing and blocking artifacts it distorts images with sharp edges, which become harder to recognize

Only 1 or 3 color channels of 8/12-bit depth are supported

Does not offer transparency preservation for images (no separate alpha-channel)

What is JPEG2000?

It’s easy to assume based on name alone that JPEG2000 (or J2K) is similar in nature to JPEG. The truth is, all the two has in common is name. J2K algorithm was developed 8 years later after JPEG took the stage and was seen at that time as the JPEG successor. The main idea behind JPEG2000 development was to create more flexible and more functional compression algorithm with better compression ratio.

JPEG2000 coding system is powered by a wavelet-based technology, which allows to choose between mathematically lossless and lossy compression within a single architecture (and even within a single codestream). Discrete Wavelet Transform (DWT) processes the image as a whole, which prevents blocking artifacts compared to JPEG.

The use of DWT and binary arithmetic coder allowed to achieve higher compression ratio compared to JPEG, especially at low bitrates. Although the compression performance was cited as a primary driver for the developers’ activity, in the end applications have been attracted to it by its other advantages.

The codestream obtained after compression is highly-scalable due to the use of EBCOT scheme (Embedded Block Coding with Optimal Truncation). J2K allows to select order of progression of resolution, quality, color components and position supplying multiple derivatives of the original image. By ordering codestream in various ways, applications can achieve significant performance increases or flexibly adapt to varying network bandwidth during transmission of image sequence. For example, gigapixel J2K-image can be viewed with a little delay, because only display-size version can be read and decoded from the whole file. Another example is ability to obtain visually-lossless image from the losslessly compressed master image, which can save time and bandwidth.

This format supports very large images (up to 232 – 1 on each dimension), multiple components (up to 16384 components for multi-spectral data), higher dynamic range (1–38 bits per component), where each component can have different resolution and bit depth.

Actually, JPEG2000 is a whole family of standards, consisting of 12 parts. Its first part “Core Coding System” specifies basic feature set (encoding and decoding processes, codestream syntax, file format) and is free to use without payment and license fees. Amongst additional parts are extensions giving more flexibility (extended file format JPX, Part 2), Motion JPEG 2000 (file format MJ2, Part 3), multi-layer compound images (file format JPM, Part 6), security framework (Part 8), communication protocol JPIP (Part 9), three-dimensional extension (JP3D, Part 10), etc.

Despite all its advantages, JPEG2000 format didn't become as ubiquitous as its developers thought it would be for various reasons. If we compare JPEG2000 and JPEG, J2K is more complex and computationally demanding, so until recently (before sufficient development of processors and parallel algorithms) it was too slow in many practical cases. Another problem was that neither manufacturers nor regular customers were ready to adopt it in early 2000s.

Today JPEG2000 is considered to be a niche format and is mostly seen when acquiring images from scanners, medical imaging devices, cameras, images from satellites, digital cinema, and high-end technical imaging equipment. However, now JPEG2000 have already reached maturity, have got support of many consumer software, and there are solutions to most of the possible problems. So it still has potential for growth of acceptance and popularity.

JPEG2000 Features

The most efficient way to understand the difference between JPEG and JPEG2000 is by looking at each of their features. Knowing this, helps us form a relationship between the two to highlight the differences even more. The following are some of the most important features of JPEG2000:

Single architecture for lossless and lossy compression (even within a single image file)

Highly-scalable codestream – ability to supply versions of image with different resolutions or quality from a single file

Support of very large size, multiple components, very high dynamic range (up to 38 bits per component)

High compression (especially at low bitrates)

Error resilience (robustness to bit errors when communication or storage devices are unreliable)

Fast random access to different resolutions, components, positions and quality layers

Region-of-Interest (ROI) on coding and access

Support for domain-specific metadata in JP2 file format

Very low loss of quality across multiple decoding/encoding cycles

Creation of compression image with specified size or quality

Pros and Cons of JPEG2000

JPEG2000 has some amazing features, and the advantages of using this image format over others are pretty impressive as well. Here are some of the reasons why you might want to use JPEG2000:

Has single compression architecture for both lossy and lossless compressions

One master image replaces multiple derivatives (different resolutions and quality)

Suits well for video production and working with live TV content

Works well with natural photos as well as synthetic visual content

Resilience to bit-errors.

JPEG2000 also has the following disadvantages:

It is not supported by web browsers (except Safari)

JPEG2000 is not compatible with JPEG. It takes additional time and efforts to integrate JPEG2000 into the system or a product even if it already uses JPEG algorithm

Standard open-source JPEG2000 codecs are too slow for active use

What is PNG?

PNG (or Portable Network Graphics) is another format that was created for lossless image compression. Today PNG is the most popular image format on websites, and it is also expected to be the eventual replacement of GIF format, which is still actively used for animations. Actually, the replacement of GIF was the main motivation for creating PNG format, because patented GIF required license and has well-known limit of 256 color palettes.

PNG uses non-patented lossless compression algorithm Deflate, which is a combination of LZ77 and Huffman coding. The progressiveness feature of PNG is based on optional 2-dimensional 7-pass interlacing scheme, which, however, reduce compression ratio when used.

PNG file size depends on color depth (up to 64 bits per pixel), predictive filter on precompression stage, implementation of Deflate compression, optional interlacing, optional metadata. Several options for lossy compression were developed for this format: posterization (reduction of number of unique colors), advanced palette selection techniques (reduction of 32-bit colors to 8-bit palette), lossy averaging filter.

Although GIF supports animation, it was decided that PNG should be a single-image format. However, in 2008 the extension of PNG called APNG (animated PNG) was proposed, and now it is supported by all major web-browsers except Microsoft IE/Edge. Moreover, even Edge will gain its support soon, because in December 2018 Microsoft announced using Chrome’s Blink engine in the Edge browser while discontinued development of its own proprietary browser engine EdgeHTML.

PNG has support of color correction data (gamma, white balance, color profiles). Correction is needed because the same numeric color values can produce different colors on different computer setups even with identical monitors. However, practical usage of this feature may become a problem, and this information is often removed by PNG optimization tools.

PNG Features

PNG has several main features that allowed it to become the most popular lossless format for raster synthetic images. Let’s briefly look at each one:

Lossless compression

Support of alpha-channel transparency (unique among the most popular in web image formats)

7-pass progressiveness

PNG compression algorithm is able to process true-color, grayscale, and palette-based types of images from 1-bit to 16-bit (unlike the JPEG that supports only the first two and only for 8 or 12 bits)

Several choices of trade-off between compression ratio and speed

Pros and Cons of PNG

PNG compression is a practical one and that makes it a really popular tool for storage and transmission of synthetic and computer-generated graphical images. Here are some additional advantages of this format:

Wide support by web browsers and other software

No patent issues

Alpha channel for adjustable transparency of pixels (opacity)

High dynamic range (up to 16 bits per channel)

PNG is not perfect and has its own drawbacks too:

No inherent support of lossy compression

Low compression ratio due to outdated compression algorithm

No inherent support of animation (only in extensions such as APNG)

What is better: JPEG vs JPEG2000 vs PNG

JPEG2000

Advantages

Both lossy and lossless compression

Flexible progressive decoding

Very good image compression ratio

Error resilience

Disadvantage

Not universally supported by browsers

Very high computational complexity

JPEG

Advantages

Compatible with all web browsers

Supported by almost all image processing software and devices

Very fast either on CPU or GPU

Disadvantages

No lossless mode in the original standard

Blocking artifacts

No transparency preservation

PNG

Advantages

Compatible with all web browsers

Reliable lossless compression

Full transparency control

Disadvantages

Not suitable for strong lossy compression

Low compression ratio

Conclusion

Each of these three image formats can be useful for different tasks. JPEG is compatible with most devices and hardware, so it can be used almost everywhere today though with some quality limitations. JPEG2000, on the other hand, is more useful for maintaining high quality of images and dealing with real-time TV content, while PNG is more convenient for online transfer of synthetic images. Each of them has unique properties that can be applied for storing and processing images in different situations.

Original article see here: https://www.fastcompression.com/blog/jpeg-j2k-png-review.htm

[fastcompression]

Low-latency software for remote collaborative post production

Fastvideo company is a team of professionals in GPU image processing, realtime camera applications, digital cinema, high performance imaging solutions. Fastvideo has been helping production companies for quite a long time and recently we've implemented low-latency software to offer collaborative post production.

Today, with restrictions on in-person collaboration, delays in shipping and limitations on travel, single point of ingest and delivery for an entire production becomes vitally important. The main goal is to offer all services both on-premises and remotely. We believe that in the near future we will see virtual and distributed post production finishing.

When you are shooting a movie at tight schedule and you need to accelerate your post production workflow, then remote collaborative approach is a right solution. You don't need to have all professionals on-site, via remote approach you can collaborate at realtime wherever your teammates are located. Industry trend to remote production solutions is clear and it happens not just due to the coronavirus. The idea to accelerate post via remote operation is viable and companies strive to remove various limitations of conventional workflow - now the professionals could choose a place and a time to work remotely on post production.

Nowadays, there are quite a lot of software solutions to offer reliable remote access via local networks or via public internet. Still, most of them were built without an idea about professional usage in tasks like colour grading, VFX, compositing and much more. In post production we need to utilize professional hardware which could visualize 10-bit or 12-bit footages. Skype, ZOOM and many other video conference solutions are not capable of doing that, so we've implemented the software to solve that matter.

Business goals to achieve at remote collaborative post production

You will share content in realtime for collaborative workflows in post production

Lossless or visually lossless encoding guarantees high image quality and exact colour reproduction

Reduced travel and rent costs for the team due to remote colour grading and reviewing

Remote work will allow to choose the best professionals for the production

Your team will work on multiple projects (time saving and multi-tasking)

Goals from technical viewpoint

Low latency software

Fast and reliable data transmission over internal or public network

Fast acquisition and processing of SD/HD-SDI and 3G-SDI streams (unpacking, packing, transforms)

Realtime J2K encoding and decoding (lossy or lossless)

High image quality

Precise colour reproduction

Maximum bit depth (10-bit or 12-bit per channel)

Task to be solved

Post industry needs low-latency, high quality video encode/decode solution for remote work according to the following pipeline:

Capture baseband video streams via HD-SDI or 3G-SDI frame grabber (Blackmagic DeckLink 8K Pro, AJA Kona 4 or Kona 5)

Live encoding with J2K codec that supports 10-bit YUV 4:2:2 and 10/12-bit 4:4:4 RGB

Send the encoded material via TCP/UDP packets to a receiver/decoder - point-to-point transmission over ethernet or public internet

Decode from stream at source colorspace/bit-depth/resolution/subsampling - Rec.709/Rec.2020, 10-bit 4:2:2 YUV or 10/12-bit 4:4:4 RGB

Send stream to baseband video playout device (Blackmagic/AJA frame grabber) to display 10-bit YUV 4:2:2 or 10/12-bit 4:4:4 RGB material on external display

Latency requirements: sub 300 ms

Basic hardware layout: Video Source (Baseband Video) -> Capture device (DeckLink) -> SDI unpacking on GPU -> J2K Encoder on GPU -> Facility Firewall (IPsec VPN) -> Public Internet -> Remote Firewall (IPsec VPN) -> J2K Decoder on GPU -> SDI packing on GPU -> Output device (DeckLink) -> Video Display (Baseband Video)

Hardware/software/parameters

HD-SDI or 3G-SDI frame grabbers: Blackmagic DeckLink 8K Pro, AJA Kona 4, AJA Kona 5

NVIDIA GPU: GeForce RTX 2070, Quadro RTX 4000 or better

OS: Windows-10 or Linux Ubuntu/CentOS

Frame Size: 1920×1080 (DCI 2K)

Frame Rates: 23.976, 24, 25, 29.97, 30 fps

Bit-depth: 8/10/12 (encode - ingest), 8/10/12 (decode - display)

Pixel formats: RGB or RGBA, v210, R12L

Frame compression: lossy or lossless

Colour Spaces for 8/10-bit YUV or 8/10/12-bit RGB: Rec.709, DCI-P3, P3-D65, Rec.2020 (optional)

Audio: 2-channel PCM or more

How to encode/decode J2K images fast?

CPU-based J2K codecs are quite slow. For example, if we consider FFmpeg-based software solutions, they are working with J2K codec from libavcodec (mj2k) or with OpenJPEG, which are far from being fast. Just test that software to check the latency and the performance. It's not surprizing, as soon as J2K algorithm has very high computational complexity. If we implement multiple threads/processes on CPU, the performance of J2K solution from libavcodec is still unsuffcient. This is the problem even for 8-bit frames with 2K resolution, though for 4K images (12-bit, 60 fps) the performance is much worse.

The reason why FFmpeg and other software are not fast at that task is obvious - they are working on CPU and they are not optimized to be high performance software. Here you can see benchmarks comparison for J2K encoding and decoding for OpenJPEG, Jasper, Kakadu, J2K-Codec, CUJ2K, Fastvideo codecs to check the performance for images with 2K and 4K resolutions (J2K lossy/lossless algorithms).

Maximum performance for J2K encoding and decoding at streaming applications could be achieved at multithreaded batch mode. This is a must to ensure massive parallel processing according to JPEG2000 algorithm. If we do batch processing, it means that we need to collect several images, which is not good for latency. If we implement batch with multithreading, it improves the performance, but the latency gets worse. This is actually a trade-off between performance and latency for the task of J2K encoding and decoding. For example, at remote color grading application we need minimum latency, so we need to process each J2K frame separately, without batch and without multithreading. Though in most cases it's better to choose acceptable latency and get the best performance with batch and multithreading.

Other info from Fastvideo about J2K and digital cinema applications

JPEG2000 codec on GPU

Fast FFmpeg J2K decoder on NVIDIA GPU

MXF Player

Fast CinemaDNG Processor software on GPU

BRAW Player and Converter for Windows and Linux

Original article see at: https://www.fastcompression.com/blog/remote-post-production-software.htm Subscribe to our mail list: https://mailchi.mp/fb5491a63dff/fastcompression