Human-Machine Vision Collaboration Based Rate Control Scheme for VVC

Le résumé fourni par la source

With the widespread adoption of smart terminals, compressed video is increasingly utilized in the receiver for purposes beyond human vision. Conventional video coding standards are optimized primarily for human visual perception and often fail to accommodate the distinct requirements of machine vision. To simultaneously satisfy the perceptual needs and the analytical demands, we propose a novel rate control scheme based on Versatile Video Coding (VVC) for human-machine vision collaborative video coding. Specifically, we employ the You Only Look Once (YOLO) network to extract task-relevant features for machine vision and formulate a detection feature weight based on these features. Leveraging the feature weight and the spatial location information of Coding Tree Units (CTUs), we propose a region classification algorithm that partitions a frame into machine vision-sensitive region (MVSR) and machine vision non-sensitive region (MVNR). Subsequently, we develop an enhanced and refined bit allocation strategy that performs region-level and CTU-level bit allocation, thereby improving the precision and effectiveness of the rate control. Experimental results demonstrate that the scheme improves machine task detection accuracy while preserving perceptual quality for human observers, effectively meeting the dual encoding requirements of human and machine vision.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.