Despite significant advances in image compression, existing evaluation metrics remain poorly aligned with human visual perception-particularly under extremely low bitrates, where reconstructed images often suffer from abstract distortions or semantic degradation that are difficult for conventional metrics to capture. To address this limitation, we propose MG-VLQA, a novel multi-granularity quality assessment framework that leverages VisionLanguage Models (VLMs) to evaluate image reconstruction fidelity through the lens of semantic consistency with the original caption. Our method formulates a suite of captionderived questions spanning three complementary dimensions: (1) entity presence (semantic completeness), (2) detail fidelity (local appearance accuracy), and (3) inter-entity interactions (relational coherence). By simulating human-like perceptual judgment via VLMbased question answering and semantic similarity scoring, MG-VLQA provides a more interpretable, fine-grained, and perceptually relevant assessment of compression quality. Extensive experiments across multiple datasets and codecs demonstrate that our metric achieves higher correlation with human judgment and offers superior discriminative power.

In this work, we propose a deep learning-based adaptive quantization method to promote video coding performance. Due to inter-prediction and reference mechanism, the block-level quantization parameter (QP) not only influences current block distortion but also has complex temporal propagation effects on subsequent coding frames. Our idea is to utilize a deep network to model the complex temporal propagation relationship of quantization. As shown in Fig. 1, the deep network directly predicts all block-level QPs of the frame for the traditional encoder without changing the standard decoder. Since our network deploys only on the encoder side and has low inference complexity, it can be easily applied in practice. In addition, we use a learned coding network as a proxy of the traditional codec to train our network.

Due to differences in frame structure, existing multi-rate video encoding algorithms cannot be directly adapted to encoders utilizing special reference frames such as AV1 without introducing substantial rate-distortion loss. To tackle this problem, we propose a novel bayesian block structure inference model inspired by a modification to an HEVC-based algorithm. It estimates the posterior probabilistic distributions of block partitioning, and adapts early terminations in the RDO procedure accordingly. Experimental results show that the proposed method provides flexibility for controlling the tradeoff between speed and coding efficiency, and can achieve an average time saving of 36.1% (up to 50.6%) with negligible bitrate cost.

Summary form only given. To better exploit spatial correlations in a video frame, the HEVC video coding standard has introduced many intra prediction modes and a recursive quadtree-based coding unit (CU) structure. As a result, the complexity of Rate-distortion optimized (RDO) HEVC intra mode selection is significantly higher. Many techniques have been proposed to expedite the intra mode selection process to achieve a good overall trade-off between complexity and RD performance. In this paper, we proposed a fast intra decision algorithm based on Hadamard Transform. The algorithm consist of three parts: SATD calculation reduction, adaptive intra candidate selection, and SATD based early termination. Experiments conducted using the HEVC common test conditions show an average of 56.4% (up to 64.1%) time saving with only 1.2% increase in Bjontegaard delta rate (BD-rate) using the proposed algorithm.

The High Efficiency Video Coding (HEVC) standard achieves 50% improvement incompression efficiency over the widely used H.264/AVC standard at a cost of much higher complexity. The increase in complexity is due to, among other factors, the time needed to findthe optimal partition structure among the more flexible possibilities for the coding units (CUs) and prediction units (PUs). Many classification based algorithms have been proposed to reduce this partition decision time, but the features that can be acquired from current HEVC encoding order may not be sufficient to control the loss in coding efficiency. In this paper, we proposed an Early-Split (ES) order for HEVC encoding, where the encoder checks the split mode before the non-square PU partition modes and utilizes the encoding output of the subCUs to expedite subsequent encoding. Experiments show that the proposed algorithm achieved an average of 48% saving in encoding time with only 0.92% loss in the coding performance.