This paper introduces the Spatio-Temporal Adaptive Context Optimization (STACO) method, which enhances the quality of contextual prediction across various resolutions, essential for subsequent compression. The STACO takes predicted contexts$C_t^{\{1,2,3\}}$as input and improves their quality by aligning them better with decoded features$f_{t}$, thus boosting coding efficiency. The STACO comprises Quality Perception Units and Consistency Synergy Modules, arranged in a hierarchical stacked architecture. This multi-scale design enables simultaneous processing of contexts at different spatial resolutions and facilitates information exchange through upsampling and downsampling. Enhanced contexts$\tilde{C}_{t}^{\{1,2,3\}}$are output after passing through residual connections, ensuring better alignment with reconstructed features. Using VTM-11.0 as anchor, the STACO significantly improves compression efficiency on common test condition (CTC) in HEVC, achieving an average BD-rate reduction of 17.99% for PSNR and 43.84% for MS-SSIM. By incorporating spatial quality mapping and temporal propagation, STACO offers a significant advancement in video compression.

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.