Hierarchical Bi-directional Temporal Context Mining for Improved Video Compression

Bidirectional video compression leverages information from both past and future frames to assist in compressing video frames. In this paper, we propose a novel multi-scale bidirectional context-aware adaptive contextual video compression framework. This framework extracts bidirectional contextual information across multiple scales and dynamically adjusts video frame encoding based on the temporal interval differences between bidirectional frames. Additionally, we introduce a bidirectional encoding and decoding scheme, which adopts a “Close To One” access order. Experimental results demonstrate that our proposed method outperforms traditional video coding standard H.265/HEVC-HM, as well as advanced deep learning-based video coding frameworks like DCVC-TCM and B-CANF.