JPEG XS Decoder on GPU

Updated: October 1, 2026

Fastvideo JPEG XS decoder is a performance-oriented implementation of JPEG XS decoding on NVIDIA GPUs, fully parallelized on CUDA. It targets low-latency, visually lossless 4K workflows in real time, where JPEG XS provides light compression for live production and transport. We offer the decoder only: a JPEG XS encoder is not part of the Fastvideo SDK.

How these numbers were measured. Fastvideo benchmarks are measured on NVIDIA GPUs, timing pure GPU processing at the resolution, bit depth, format and quality stated on this page. See the full benchmark methodology and test environment and the Fastvideo SDK benchmark report (PDF). Our tools are open source on GitHub and built on NVIDIA CUDA.

Check a single module. Every module in the Fastvideo SDK comes with a command-line sample. Run it on your own images on your own GPU and compare the result with the figures published here.

Check the whole pipeline. A module in the middle of a pipeline cannot be judged on its own: its input is hard to prepare, and its output is an intermediate product you cannot look at. What matters is how it changes the final image and the total time of the schema. Two of our applications run the complete pipeline and report the time of every stage — FastVCR (live cameras or RAW files from disk, so no camera is required) and Fast CinemaDNG (DNG files, offline), both built on our GPU RAW Processor pipeline. The benchmark module is included in the free builds of both applications.

Detailed benchmark data. Measured results are published on our benchmark pages: JPEG codec benchmarks, all benchmarks.

We wrote these algorithms ourselves

Image processing has always been a trade-off between quality and speed. The methods that give a good result take minutes per frame; the fast ones leave halos and artifacts. The reason is the compute budget: on a CPU there was never enough of it, so algorithms were simplified — and much of the industry still runs on simplifications accepted decades ago.

Fastvideo’s codecs and ISP modules are neither ports of that legacy nor wrappers around third-party code. We designed — and in many cases invented — the algorithms behind our demosaicing, denoising, tone mapping and JPEG/JPEG2000 codecs, and wrote them for CUDA from the start, on top of image-processing expertise built since 2009. An algorithm moved to the GPU still carries the compromises it was simplified for; ours were never made under that constraint. On a modern GPU the budget is large enough that, for practical workloads, the choice between quality and speed no longer has to be made.

That is why you get a complete, tuned imaging solution rather than a bag of GPU primitives — and why, when your pipeline needs something specific, we can change the algorithm itself, not just call it.

Key features of the JPEG XS decoder on the GPU

JPEG XS decoding of grayscale and color images with arbitrary width and height
Bit depth: 8-16 bits per channel
Reversible discrete wavelet transform: Le Gall 5/3
Reversible color transform
Chroma subsampling modes: 4:4:4, 4:2:2, 4:2:0
Asymmetrical DWT: up to two vertical decomposition levels and up to eight horizontal levels
Rectangular precincts support
Entropy decoding
Typical compression ratio of JPEG XS images: from 2:1 to 20:1
Data input: JPEG XS images from HDD/RAID/SSD or CPU/GPU memory
Data output: final decompressed images in HDD/RAID/SSD or CPU/GPU memory
Optimized for the latest NVIDIA GPUs
Performance is much better than CPU-based JPEG XS decoder
Optional integration with OpenGL
Compatible with 64-bit Windows-10, Linux

We can integrate JPEG XS decoder in your image processing pipeline to perform the whole job completely on the NVIDIA GPU. Please check the detailed info about our GPU Image & Video Processing SDK to evaluate what we can do on the GPU.

JPEG XS decoding benchmarks at multithreaded mode on different NVIDIA GPUs

How fast is JPEG XS decoding on the GPU?

JPEG XS decoding speed (fps) across NVIDIA GeForce RTX 2070 Super, 2080 Ti and 4090, by image resolution.
Image for decoding	GeForce RTX 2070S	GeForce RTX 2080ti	GeForce RTX 4090
2K image, 24-bit, 4:4:4, cr = 12.4	880 fps	1225 fps	1740 fps
4K image, 24-bit, 4:4:4, cr = 12.4	301 fps	425 fps	685 fps

Patents and RAND

Please bear in mind that JPEG XS contains patented technology which is made available for licensing via the JPEG XS Patent Portfolio License (JPEG XS PPL). This license pool covers essential patents owned by Licensors for implementing the ISO/IEC 21122 JPEG XS video coding Standard and is available under RAND terms. You can find more info at https://www.jpegxspool.com

We've implemented the high performance JPEG-XS decoder on NVIDIA GPU as an accelerated solution for standard CPU-based JPEG XS project from iso.org (Part 5 of the International Standard ISO/IEC 21122-5:2020), which is really slow on the CPU. That was done to show the potential for GPU-based speedup for such a codec. We can offer our customers high performance JPEG XS decoder on NVIDIA GPU, though all questions concerning licensing of JPEG XS technology the customer has to settle with JPEG XS patents owners.

About the author

Fyodor Serzhenko, PhD, is the founder and CEO of Fastvideo. He earned his PhD at the Moscow Institute of Physics and Technology (MIPT) in 1993. Since 2009 he has led the development of Fastvideo’s GPU-accelerated image codecs and ISP modules, including the Fastvideo SDK. Connect on LinkedIn.

Why you can trust these results

Fastvideo has built GPU-accelerated image processing software since 2009. Our codecs comply with the standards they implement — JPEG (ITU-T T.81 / ISO IEC 10918) and JPEG2000 (ITU-T T.800 / ISO IEC 15444) — and our tools are open source on GitHub. Every performance figure we publish is reproducible: see our benchmark methodology, download the Fastvideo SDK benchmark report (PDF), and measure it on your own GPU.