Lossless Image/Video Embedded Compression for Memory Bandwidth Saving of AI Applications

Artificial Intelligence (AI) has gradually become a part of our daily life. Nevertheless, it also causes considerable memory bandwidth and memory access power especially for image/video AI applications. In this paper, an efficient lossless embedded compression (EC) is proposed to save the memory bandwidth of an AI system. It consists of two core techniques: Hybrid Prediction and Partition-based Grouping. Hybrid Prediction can transform all pixels of an 8x8 block to efficient residuals. Partition-based Grouping can further classify them into smaller groups for better compression ratio. The experiment results show that this study presents better performance than the other sophisticated EC algorithms. This study achieves a better lossless compression ratio of 2.21, saving the memory bandwidth of an AI system by 54.8%. In addition, the visual quality of image/video can be fully preserved.