GPU Image Processing for XIMEA xiQ cameras with USB-3.0 interface

Updated: September 17, 2026

Fastvideo software processes XIMEA xiQ USB3 cameras on the GPU in real time with the FastVCR application, running the full ISP for these high-resolution, high-speed sensors. Demosaicing, color, and compression run on NVIDIA GPUs, so xiQ streams are handled live without a CPU bottleneck.

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: all benchmarks.

Different XIMEA interface or integration? See the xiB PCIe cameras, the FastVCR application for XIMEA, or the XIMEA CamTool GPU plugin. This page covers xiQ USB3 cameras.

Image sensors of color cameras are covered with Bayer filter arrays to recover color information later at post processing stage. So after demosaicing (debayering) the amount of data will be tripled. Trying to do that even on the fastest CPU we will see that performance for image processing would be far from real-time due to high frame rate or high resolution of image sensors. Below we list some frequently used image sensors to deal with:

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.

Image sensors for XIMEA xiQ cameras

CMV300: 640×480, 500 fps (8-bit), 250 fps (10-bit)
CMV2000: 2048×1088, 340 fps (10-bit), 70 fps (12-bit)
CMV4000: 2048×2048, 180 fps (10-bit), 37 fps (12-bit)
e2V EV76C560: 1280×1024, 60 fps (10-bit)
Onsemi PYTHON 1300: 1280×1024, 210 fps (10-bit)

FastVCR software for real-time image processing on GPU for xiB cameras

Image acquisition
Frame unpacking for 10-bit and 12-bit modes
Image linearization
Dark frame subtraction (FPN)
Flat-Field Correction (shading correction)
Bad pixel removal
White Balance / AWB
Adaptive Exposure and Gain control
High quality demosaicing with MG algorithm
Color correction with matrix profile or DCP profile
Highlight recovery
Exposure correction (brightness control)
Curves and Levels
Denoising
Rotation to 90/180/270 degrees and flip/flop
Crop
Resize (downscale and upscale)
Rotation to an arbitrary angle
Sharpening (local contrast)
Gamma transform
JPEG compression and storage on SSD
Optional conversion to NV12 and h264/h265/av1 video encoding
Automatic real-time partitioning of AVI/MP4 video files to the specified file size

Compatibility

CUDA-12.3 for Windows/Linux, NVIDIA GeForce and NVIDIA RTX
CUDA-11.4 for NVIDIA Jetson NX, AGX Xavier, Orin
XIMEA USB3 and PCIe cameras
Processing of 8-12-bit RAW/PGM/TIFF images from SSD

Advantages of GPU image processing for xiQ cameras

Why use GPU image processing for XIMEA xiQ cameras?

Per-pixel offset and shading correction
High quality demosaicing
Low latency output to monitor via OpenGL
Fast image processing at very high fps
Real-time image processing for multiple camera systems
Low CPU usage

Software downloads

FastVCR software for XIMEA cameras on Windows and CUDA-12.6. The software can also work with PGM/RAW/TIFF frames from SSD on Windows.
FastVCR software for XIMEA cameras on Jetson Nano and CUDA-10.2
FastVCR software for XIMEA cameras on Jetson NX/AGX Xavier and Orin on CUDA-11.4
PDF manual for FasVCR software
XIMEA SDK for Windows/Linux/ARM

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.