Video coding achieves efficient compression by exploiting the spatial and temporal correlations within the video signal. However, the noise in the source signal corrupts such correlation and impairs the coding performance. Motion compensated temporal filter (MCTF) [1] is a pre-processing tool that removes certain noise from the source signal, thereby enhancing temporal correlations among adjacent frames. Although numerous efforts have been made to optimize MCTF, MCTF still lacks flexibility in filtering for diverse video content, and its filtering efficiency is still limited. In this paper, we propose the Content-Aware MCTF method (CAMCTF) to enhance the filtering adaptability of MCTF for diverse video contents. The CAMCTF adaptively adjusts the filtering block sizes based on the Sum of Square Error (SSE) and Motion Vector (MV) information calculated during the motion estimation (ME) process in MCTF, and offset weighting method is applied to improve the prediction quality of filtering blocks. Performance was evaluated on top of Versatile Video Coding (VVC) reference software VTM-23.4. The VVC Common Test Conditions (CTC) [2] with QPs 22, 27, 32, 37 are used. As shown in Table 1, CAMCTF achieves an overall of 1.07% and 0.79% luma BD-rate gains in Random Access (RA) and Low Delay (LD) configurations, respectively. The encoder complexity is 112% and 110% for RA and LD configurations, respectively, without decoder complexity increasing.

Efficiently improving the accuracy of motion vector (MV) in merge candidate list is a critical issue in terms of the advanced inter coding technologies. The decoder side motion vector refinement (DMVR) and merge mode with motion vector differences (MMVD) are utilized to refine the MV obtained from merge mode in Versatile Video Coding (VVC). Nevertheless, both DMVR and MMVD operate under the assumption of symmetric motion when adjusting bi-prediction MV. This assumption may result in inaccuracies adjusting where the motion is asymmetric, leading to imprecise MV. To address this issue, we propose the asymmetric motion vector refinement (ASMVR) approach to refine asymmetric motion more accurately for future video coding in this paper. Specifically, ASMVR is formulated by the asymmetric MMVD (asy-MMVD) and asymmetric DMVR (asy-DMVR) schemes, which are compatible with MMVD and DMVR in VVC respectively. Four asymmetric MV refinement templates are devised to capture varying motion scenes, and the optimal one is derived through bilateral matching and rate-distortion optimization. Moreover, meticulously designed fast algorithms are implemented to bypass unnecessary candidate evaluations, thereby effectively reducing both encoding and decoding complexities. The simulation result shows that on top of the VVC Test Model (VTM-22.1), ASMVR achieves 1.52% BD-rate saving for random access (RA).

In-loop filter is a crucial module in video coding, which can improve both subjective and object quality of reconstructed videos. In this paper, a new sample-based classification method is first proposed using features extracted from different stages of the existing in-loop filter process. Based on this method, an adaptive three-layer Quad-tree Based Sample Refinement Filter (QSRF) algorithm is designed to further improve the coding efficiency. Experimental results show that the proposed QSRF algorithm achieves 0.39%, 0.77% and 0.70% BD-rate savings for random access, lowdelay B and lowdelay P configurations compared to AVS3 reference software, respectively. Moreover, the proposed method can also improve visual quality of reconstructed videos significantly.