CUDA Resizer from Fastvideo
We have developed extremely fast software to scale grayscale and color images on GPU. This is fast CUDA Resizer implementation for upscale and downscale, which is now a part of our GPU Image & Video Processing SDK. Image resizer on CUDA shows outstanding performance with superior quality and this is the best solution for your HPC systems for realtime image processing.
CUDA Resizer Features
CUDA Resizer benchmark on NVIDIA GeForce GTX 1080
High quality image resize for color Full HD (1920×1080, 24-bit) image to final resolution 960×576 can be done on NVIDIA GeForce GTX 1080 GPU at frame rate 3000 fps. We recommend to use our resize solution on CUDA together with other components from our Image Processing SDK to be able to do fast and high quality resize for grayscale or color images.
CUDA Resize for JPEG images
If we need to resize JPEG images on GPU, we need to decode these images first. That could be also done on GPU with the aid of CUDA JPEG Codec. After that we can apply CUDA Resize and some optional transforms on GPU like Crop, Rotation, Sharpening, etc. At the final part of such a pipeline we usually apply JPEG Encoder to get output image in jpg format. Here you can see more info about JPEG Resizer benchmarks on NVIDIA Tesla V100 and review for the latest solutions and benchmarks on GPU and FPGA.
Recently we've got significant performance boost for JPEG Resize with CUDA MPS on Linux. Now such a solution is much faster than CPU and FPGA implementations for JPEG Resize.
We license CUDA Resizer and other components of GPU Image & Video Processing SDK to software developers, camera manufacturers, internet providers, software integrators, etc. Our SDK is utilized in wide range of high performance imaging applications. SDK evaluation version, documentation, licensing info and quotation are available upon request. We are also offering custom software design according to agreed specification. If you need to get significant speed up for your image processing application, don't hesitate to contact us.
Roadmap for further improvements of CUDA Resizer