Evaluation of image sequence (video) compression with an embedded processing system for future space applications

Abstract

In recent years, the accessibility to Graphics Processing Unit (GPU)s and hardware accelerators for space missions has increased drastically with implementable Commercial Off-The-Shelf (COTS) solutions. As a result, the onboard computing power has increased for many space missions, and the performances of the different video compression methods have been improved. With such evolution in computing resources, it is crucial to analyze the different methods and record their performances, to find efficient compression methods in this new configuration. To find efficient compression methods, we developed a benchmark, to assess multiple methods on their compression ratios, power consumption, execution time and the difference of quality between the input and output images on a low resource configuration, adapted for space missions. From the analysis led in this thesis 4, on the standard codecs, the more lossless the compression, the higher the benefits from the acceleration. The machine learning approach shows promising results for the future, and the Consultative Committee for Space Data Systems (CCSDS) 122 despite the GPU acceleration was outperformed by Advanced Video Coding (H.264) and High-Efficiency Video Coding (H.265).