Neural network-based in-loop filtering significantly enhances video compression efficiency. However, high computational complexity hinders their deployment in real-time and ultra-high-definition scenarios. To address this, we propose a lightweight CNN-based in-loop filter for the luma component. In terms of model design, we utilize a U-Net-like architecture to learn the residual signal, incorporating depthwise separable 3 × 3 convolutions and 1 × 1 convolutions to reduce computational complexity, which results in a low complexity of only$37.707 \text{kMACs} /$pixel. For deployment optimization, we implement memory linearization to improve cache efficiency and combine blocked matrix multiplication with SIMD to maximize parallelism, ensuring cross-platform compatibility without third-party libraries. Experimental results on AVS4 EVM-0.9 (All-Intra) on a CPU platform show that the proposed method achieves BD-rate reductions of$1.36 \%, 0.35 \%$, and 0.34% for$\mathrm{Y}, \mathrm{U}$, and V components, respectively. Furthermore, the optimizations lead to a 91.5% reduction in decoding time, resulting in a decoding complexity of 7757% compared to the anchor.

Video post-processing can significantly enhance compressed video quality. Traditional methods are mostly based on handcrafted designs, such as deblocking and deringing algorithms, which rely on fixed heuristic rules and exhibit limited flexibility and adaptability. In recent years, with the rapid development of deep learning, Neural Network-based (NN-based) video post-processing methods have demonstrated remarkable coding performance. Among these, Transformer-based or multi-frame joint enhancement methods have improved reconstruction quality but still heavily depend on the prediction of known pixels, making it difficult to effectively restore high-frequency texture information lost due to the lossy compression. In contrast, diffusion models leverage their powerful generative priors and progressive denoising mechanisms to synthesize more natural and realistic high-frequency details, providing a promising solution for video post-processing. Inspired by recent advancements in conditional generative modeling, we propose a diffusion-based post-processing filter. The design incorporates a quantization parameter adaptive module and a block-based inference strategy with overlapping blocks to balance visual quality and efficiency. Experimental results demonstrate that the proposed method achieves significant improvements in both subjective and objective quality on the VTM-11.0.

Since the introduction of hierarchical picture prediction structure in the advanced video coding (AVC), the hierarchical coding structure (HCS) has been widely adopted and continuously improved in video coding standards. Correspondingly, the HCS-based bit allocation methods in rate control have also emerged endlessly. Considering that pictures in higher temporal levels (TLs) of HCS usually refer to pictures in lower TLs, most methods tend to allocate more bits to pictures in lower TLs. However, these methods do not fully consider the correlation of picture quality in different TLs, which leads to the bit allocation waste and the coding performance degradation. To address this issue, we propose a fast bit allocation refinement method that can adapt to different video rate control approaches. Fig. 1 shows the overall framework of the proposed method. In general, our method is to appropriately adjust the bit allocation of pictures in lower TLs according to the relationship between the quality of pictures in different TLs. Specifically, based on the hyperbolic rate-distortion (RD) model and initial allocated bits, the quality of picture in higher TLs is first predicted and then used to estimate the quality of picture in lower TLs. Subsequently, the bits of picture in lower TLs are derived using estimated quality and its RD model. Finally, the final allocated bits of picture in lower TLs are adjusted by comparing the estimated and initial allocated bits. Experimental results show that our method can improve the coding performance of different rate control methods without introducing latency and encoding complexity.

In-loop filter has been comprehensively explored during the development of video coding standards to suppress compression artifacts. However, the existing in-loop filters in Versatile Video Coding (VVC) mainly take advantage of the image local similarity. Although some non-local based in-loop filters can make up for this short-coming, the unsupervised parameter selection scheme, which is widely used by non-local filters, limits the content adaptability. Given this, we propose a parametric non-local in-loop filter (PNLF) that fully considers the non-local characteristics and trains the filter coefficients based on the video content. In the filtering process, the reference samples based on the non-local similarity are first derived for each to-be-filtered sample. Then to-be-filtered samples are grouped into specific classes based on multiple features. For each class, filter coefficients are online trained in the encoder and transmitted to the decoder. Finally, the filtering process is conducted using the online-selected coefficients. Simulation results reveal that the proposed approach achieves 0.70%, 1.43%, and 2.09% bit-rate savings on average compared to VTM-11.0 under All Intra (AI), Random Access (RA), and Low-Delay B (LDB) configurations, respectively. The sequences used in the experiment include Class AI, A2, B, C, D, E, F, and SCC. Compared to the non-local structure-based filter (NLSF) [1], our proposed PNLF with fast block matching scheme [2] applied on B-frames and P-frames can achieve better performance gain with lower software and hardware complexity under RA and LDB configurations.