Software for Jetson TK1 GPU

Updated: August 27, 2026

Jetson TK1 is NVIDIA’s first mobile processor which includes high-performance Kepler GPU architecture. TK1 offers very high CPU and GPU performance to mobile computing. PC class imaging applications that require high performance, low energy consumption and large amounts of memory can now be developed for mobile devices with Jetson TK1.

Choosing a Jetson generation? This page covers the earliest NVIDIA Jetson TK1 (Tegra K1, Kepler). For current modules use Jetson Orin ISP SDK or Jetson Xavier SDK. Other earlier: Jetson TX2 (Tegra X2), Jetson TX1 (Tegra X1).

We have ported our high performance image processing SDK to Tegra and now we can offer high performance imaging solutions for Nvidia’s quad-core TK1 GPU.

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.

Jetson TK1 benchmarks for image processing on GPU

What image processing performance does the Jetson TK1 deliver?

We have done measurements of kernel times for the most important components of GPU image processing SDK from Fastvideo. We utilized images with Full HD resolution (1920×1080) and got the following averaged results:

HQLI Debayer (8-bit) – 1.1 ms
DFPD Debayer (8-bit) – 5.2 ms
JPEG compression (8-bit, quality 90%) – 9.6 ms
JPEG compression (24-bit, quality 90%, 4:4:4) – 15.8 ms
JPEG decompression (24-bit, quality 90%, 4:4:4) – 17.8 ms
Combined DFPD Debayer + JPEG compression (quality 90%, 4:4:4) – 26 ms
LUT (8-bit for each color component) – 1.1 ms
Color image sharpening (sigma=0.4) – 1.5 ms
Color image crop from 1920×1080 to 960×540 – 0.4 ms
Color image resize from 1920×1080 to 960×540 – 10.8 ms
Color image resize from 1920×1080 to 1919×1079 – 20 ms

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, offline). The benchmark module is included in the free builds of both.

Detailed benchmark data. Measured results are published on our benchmark pages: Jetson / Xavier benchmarks, all benchmarks.

Results show that on Jetson TK1 GPU one can do real-time and high quality image processing for Full HD cameras at 30 fps, including full image processing pipeline, starting from camera raw data to compressed MJPEG video stream.

The fastest real-time solution for camera application could be achieved in the case if we do direct JPEG compression of RAW data. We can get 100 fps performance for Full HD resolution (quality 90%) which corresponds to data rate 200 MPix/s for 8-bit data. For viewing compressed data one can do JPEG decompression and debayering on desktop GPU in off-line which is straightforward with our image and video processing SDK.

Benchmarks for Fastvideo SDK on Tegra X1, X2, Xavier

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.