Adaptive Surveillance Video Compression With Background Hyperprior
Yu Qian Zhao, Song Yuan Tang, Mao Ye · IEEE Signal Processing Letters · 2024
Neural surveillance video compression methods have demonstrated significant improvements over traditional video compression techniques. In current surveillance video compression frameworks, the first frame in a Group of Pictures (GOP) is usually compressed fully as an I frame, and the subsequent P frames are compressed by referencing this I frame at Low Delay P (LDP) encoding mode. However, this compression approach overlooks the utilization of background information, which limits its adaptability to different scenarios. In this paper, we propose a novel Adaptive Surveillance Video Compression framework based on background hyperprior, dubbed as ASVC. This background hyperprior is related with side information to assist in coding both the temporal and spatial domains. Our method mainly consists of two components. First, the background information from a GOP is extracted, modeled as hyperprior and is compressed by exiting methods. Then these hyperprior is used as side information to compress both I frames and P frames. ASVC effectively captures the temporal dependencies in the latent representations of surveillance videos by leveraging background hyperprior for auxiliary video encoding. The experimental results demonstrate that applying ASVC to traditional and learning based methods significantly improves performance.