Efficient feature coding based on performance analysis of Versatile Video Coding (VVC) in Video Coding for Machines (VCM)

Conventional video coding standards offer efficient compression of traditional 2D images. In particular, versatile video coding (VVC), which is the latest video coding standard, achieves very high compression efficiency, while maintaining high visual quality for humans. On the other hand, video coding for machines (VCM), which is developed as a new style of a video coding standard, mainly targets efficient compression of features extracted from deep neural networks. It generally employs VVC for feature coding. However, since VVC was developed for traditional images, an influence of the VVC based feature coding on VCM is not clear. Therefore, this paper proposes efficient tool combination by analyzing performance of VVC coding tools for the VCM feature coding, and then applies it into video captioning, which automatically generates natural language descriptions from videos. Experimental results show that the proposed tool combination is very efficient, in terms of coding performance and encoding complexity.