Video Coding for Machines With Neural-Network-Based Chroma Synthesis

Video coding for machines is an emerging area within video compression technology that has recently attracted considerable research attention. Within the ISO/IEC standardization activities, efforts are underway to develop a new standard optimized for machine vision tasks rather than just for human-oriented video consumption. In this context, the novel contribution of this work is the application of neural networks to perform chroma synthesis at the decoder side, thus eliminating the need for direct chroma transmission. This concept has been implemented and validated in the Video Coding for Machines Reference Software (VCM-RS), a test model developed by the MPEG Video group, which is briefly described for completeness. Experimental results reported in this paper show that our approach significantly reduces the number of bits required for video representation. An average bitrate reduction of 12% in Random Access End-to-End configurations for object tracking tasks is reported (for certain video sequences, bitrate reduction even up to 60%). The presented proposal has been accepted in the current version of the specification of the upcoming VCM standard.