Histogram computation on CUDA
Updated:
Histogram computation is a frequently utilized task in image and video processing. As soon as histogram algorithm could be run in parallel, one can do that on GPU to get very high performance. We've implemented CUDA-based histogram kernels and now they are the part of Fastvideo Image & Video Processing SDK.
Applications for CUDA histogram
What is GPU histogram calculation used for?
- Camera applications
- Digital Cinema
- High speed imaging
- Raw image processing
Features
- Input images: 8/12/16-bits per channel images in RGB, PGM, PPM, BMP, YUV, JPG, JP2 formats, byte array in CPU or GPU memory
- Input video: MJPEG (AVI), MJPEG2000
- Histograms: 256-bin, 512-bin, 1024-bin
OS Supported (64-bit)
- Windows-10/11
- Linux Ubuntu, L4T
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.