GPU-accelerated DXT and JPEG compression schemes for low-latency network transmissions of HD, 2K, and 4K video

•We propose a design of efficient parallel JPEG encoding and decoding for GPUs.•Efficiency and performance study of GPU-accelerated JPEG and DXT compressions.•Compression is applicable for HD, 2K and 4K video processing on commodity HW.•Study of applicability in medicine and cinematography.•Deployment of GPU-accelerated compressions in trans-Atlantic networks.

Low-latency transmissions of high resolution video such as HD, 2K, or 4K over both Internet and private IP networks have found a foothold in many interactive applications, ranging from collaborative environments in science and medicine to the arts and entertainment industry. In this paper we demonstrate how the power of commodity graphics processing units can be used for efficient implementation of JPEG and DXT compression. We propose an approach to fine-grained parallelization of JPEG compression and the use of auxiliary indexes for efficient decompression, which are backward compatible with the JPEG standard. In-depth performance analysis is provided to show various aspects of the proposed parallelization including the dependency on image content and on various settings of the compression algorithm, as well as the impact of compression for interactive applications in terms of end-to-end latency. The applicability of these compression schemes in medicine and cinematography has also been evaluated using double-blind ABX tests compared with uncompressed video. We describe selected successful real-world deployment use cases based on our open-source implementation within the UltraGrid framework, such as trans-Atlantic 4K interactive video streaming during the CineGrid 2011 workshop. As discussed in the paper, the proposed approaches to parallelization provide sufficient performance even for the future generation of 8K video processing systems, currently limited by availability of camera and display hardware.