Fastvideo GPU Image Processing Benchmarks
Updated:
We've implemented Image & Video Processing SDK for NVIDIA GPU to offer high performance solutions for our customers in various imaging applications. Here we present our benchmarks to let our users evaluate the performance which could be obtained on NVIDIA GPU with image processing modules from Fastvideo SDK.
OS Windows 10, CUDA-12.6, timings include GPU computations only, image processing at just one GPU for 24-bit images. JPEG encoding with compression ratio ~10:1 and subsampling 4:2:0. JPEG2000 lossy encoding with compression ratio ~12:1, subsampling 4:4:4, cb 32×32. Tone mapping with ALTMapper algorithm. Denoising algorithm is bilateral with min window.
These are algorithm-specific benchmarks:
- Benchmarks for Debayer Algorithms on GPU
- Benchmarks for JPEG2000 encoders on CPU and GPU
- Benchmarks for J2K decoders on CPU and GPU
- Fastvideo JPEG2000 vs nvJPEG2000 on RTX 4090 — open comparison with the free NVIDIA library
- Tegra Xavier benchmarks for image and video processing
You can download PDF document with detailed benchmarks for Fastvideo SDK - Fastvideo SDK benchmark report (PDF).
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.