Dynamic Temporal Reference Aggregation for Neural Video Compression
Shuhong Liao, Kexiang Feng, Zhimeng Huang, Siwei Ma, Qi Wang, Lili Chen, Chuanmin Jia · 2025
Neural Video Compression (NVC) has advanced significantly in recent years, with improvements in inter prediction techniques. In inter prediction, most NVC approaches utilize pixel information or temporal features from neighboring frames as reference information, while using optical flow to represent motion information. In this paper, we introduce an innovative and efficient method for Dynamic Temporal Reference Aggregation (DTRA). The proposed DTRA consists of two components: Temporal Information Compensation (TIC) and Feature Level Motion Information Enhancement (MIE). The TIC module generates compensation information by leveraging long-term temporal information from the decoding buffer, enriching the semantic content of the reference features and enhancing their texture details. The MIE module refines the motion features at the encoder side and divides the motion information into multiple groups for diverse motion alignment at the decoder side, thereby improving the motion compensation. Extensive experiments demonstrate the effectiveness of the proposed method, achieving an average bitrate savings of 9.67% compared to state-of-the-art (SOTA) approaches.