Efficient Learned Video Compression via Bidirectional Temporal Information Exploration

In recent years, learned video compression methods have improved substantially. However, most existing algorithms focus on exploring short-term temporal information, thus constraining the compression capability. In this paper, to further boost video compression performance, we exploit both long-and short-range temporal information and consider bidirectional temporal information. For long- and short-range temporal information exploration, we adopt temporal prior and progressive guided motion compensation. Specifically, with the continuously updating strategy, the temporal prior can provide rich mutual information between the overall prior and the current frame, facilitating Gaussian parameter prediction in the entropy model. Besides, the progressive guided motion compensation utilizes flow-to-kernel and scale-by-scale stable guiding strategy, thus achieving robust and effective inter coding. Furthermore, existing low-latency-oriented methods often suffer from strong error propagation, so we extend the framework with a bidirectional prediction scheme and propose the bidirectional temporal prior. Extensive experimental results demonstrate that our method can obtain competitive performance compared to the state-of-the-art learned video compression approaches and the standard reference software HM-16.22.