Inter prediction is a fundamental component of modern video coding, where the quality of reference frames critically affects motion compensation accuracy and overall compression efficiency. However, relying solely on reconstructed low-temporal-layer frames imposes significant limitations, as these frames often suffer from compression artifacts that degrade prediction quality. To overcome this limitation, we propose a Local-Global spatiotemporal context modeling-based virtual reference frame generation network (LGCM-Net) that synthesizes high-quality reference frames based on reconstructed frames, as shown in Fig. 1. The proposed network integrates hierarchical feature extraction with long-range dependency modeling, where QP-conditioned modulation is applied to shallow features to adapt them to quantization-induced quality variations, enabling temporally and structurally consistent reference generation closely aligned with the to-be-coded frame. Moreover, a coarse-to-fine multi-stage optical flow refinement mechanism is employed to progressively enhance motion accuracy, and a residual refiner further compensates remaining motion estimation errors and reconstruction artifacts to deliver a more accurate final prediction. The proposed method achieves$5.37 \%, 9.96 \%$, and 9.91% BD-rate reduction under the Random Access configuration in VVC reference Software (VTM-11.0_nnvc-10.0 w/o NN Coding tools) for the$\mathrm{Y}, \mathrm{U}$, and V components, respectively.

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.

In this paper, a hybrid video compression framework is proposed that serves as a demonstrative showcase of deep learning-based approaches extending beyond the confines of traditional coding methodologies. The proposed hybrid framework is founded upon the Enhanced Compression Model (ECM), which is a further enhancement of the Versatile Video Coding (VVC) standard. We have augmented the latest ECM reference software with well-designed coding techniques, including block partitioning, deep learning-based loop filter, and the activation of block importance mapping (BIM) which was integrated but previously inactive within ECM, further enhancing coding performance. We evaluate the coding performance of the proposed framework with extensive experiments on the JVET dataset compared with ECM10.0 and VTM-11.0. Due to the testing environment and the coding complexity of the ECM, we did not conduct testing on Class A. The QPs are set as 22, 27, 32, 37, and 42. Compared with ECM-10.0, our method achieves 6.26%, 13.33%, and 12.33% BD-rate savings for the Y, U, and V components under random access (RA) configuration. The traditional hybrid coding framework combined with the three coding tools can further improve compression efficiency and has great potential for performance improvement.