Intra coding plays a critical role in video compression, yet conventional directional intra prediction mainly relies on boundary pixels and fails to capture complex textures and long-range spatial correlations. Template Matching Prediction (TMP) alleviates this issue by searching similar templates in reconstructed regions without motion-vector signaling; however, its MSE/MAE-based matching and simple averaging fusion are sensitive to noise and often unreliable for complex content. This paper proposes E2ETMPN, an end-to-end template matching prediction network for intra coding. E2ETMPN jointly models template matching and multi-candidate fusion under a unified loss function. It consists of two components: (1) a CNN-based multiscale matching sub-network that searches candidate predictions from reconstructed regions at different spatial scales; and (2) a Transformer-based fusion sub-network that adaptively fuses multiple candidates with template information using attention mechanisms to generate the final prediction. Experimental results demonstrate that integrating E2ETMPN into the reference encoder achieves consistent BD-rate reductions over conventional TMP and standard intra prediction methods on standard test sequences. Notably, E2ETMPN yields larger gains on screen content and scenes with rich non-local repetitive structures, validating the effectiveness of end-to-end learning for template matching prediction in intra coding.

Video-Text Retrieval (VTR) aims to align video content with natural language descriptions and is a fundamental task in multi-modal understanding. Most existing methods model videos as uniformly sampled RGB frames, which overlooks rich temporal cues, especially motion dynamics encoded in videos. We propose Compressed Video Stream Learning for Video-Text Retrieval (CVSVTR), a framework that directly exploits information from compressed video streams to enhance retrieval performance without fully decoding videos. Specifically, CVSVTR decodes only the I-frames of each GOP and extracts appearance features using a CLIP-based encoder. Meanwhile, motion vectors and residuals are parsed from the compressed bitstream and processed by a lightweight P-frame Feature Generation (PFG) module to construct motion-aware representations for P-frames. A spatial-channel attention mechanism is further introduced to adaptively fuse appearance features with compressed-domain motion cues, compensating for temporal information missed by uniform frame sampling. Extensive experiments on the MSR-VTT and MSVD benchmarks demonstrate that CVSVTR consistently outperforms existing video-text retrieval methods across multiple evaluation metrics, validating the effectiveness of leveraging compressed video streams for efficient and accurate temporal modeling in VTR.

The JPEG standard is still widely-used in current applications. The optimization on the quantization table in JPEG standard not only can maintain the compatibility with the existing JPEG standard, and but also can improve the coding efficiency of JPEG compression. In this paper, JPEG quantization table optimization via multi-objective evolutionary algorithm based on decomposition is proposed. In the multi-objective evolutionary optimization framework, the fitness of each quantization table for JPEG compression is evaluated efficiently by the searching in a look-up table, which is constructed based on the statistics of each DCT band in a pre-defined manner. Then, the MOEA/D algorithm is utilized to minimize the rate and distortion simultaneously. After the multi-objective optimization, a set of non-dominated solutions (quantization tables) can be obtained. Therefore, our method can provide several optimal solutions to address the multiple-rate requirement in applications in one running time. The experimental results on the classic Kodak24 dataset demonstrate the superiority of our method in terms of coding efficiency and subjective quality.

In this paper, we propose a novel low-rank based non-local image denoising method for HEVC video compression with the strategy of gathering non-local patches in the rectified domain. Owing to the irreversible quantization, image compression can be considered as adding noises into the original image, causing the distortion between the original image and the de-compressed image. Current non-local collaboration based image denoising methods collect K-nearest patches from image interior with the underlying translational motion model, and restores the corrupted content from the similar image patches. However, these methods are usually not suitable for image content with repeated patterns with different scales due to the viewing perspective. To address this problem, we derive the plane orientation utilizing vanishing points, which can be determined through line segment detection and clustering. Vanishing points are used to construct a specific homographic transformation to model patch correspondence in rectified domain. With this transformation, the regularity in image can be extracted based on SIFT features and mean-shift clustering in the rectified domain, and can provide useful guidance for patch gathering. Thus, our method is especially suitable for urban building scenes. Our experimental results show that the non-local denoising in rectified domain can further improve the average PSNR by up to 0.32dB comparing to those non-local methods on Urban100 dataset compressed by HEVC standard.

In this paper, we propose an enhanced intra block copy method for intra prediction in HEVC through planar perspective transformation for urban building scenes. Since the imaging plane of the camera is not always fronto-parallel to the facades of buildings (object plane) in the image, repetitive patterns on object plane in 3D space appear with scale shift in the image, and the corresponding redundancy cannot be removed through conventional intra block copy based on translational motion model. To solve this problem, we start with theoretic analysis to perspective transformation, and get that the two-parametered planar perspective transformation is the cause of scale shift. Therefore, intra block copy can be enhanced via rectification through planar perspective transformation, whose detailed parameters are derived through vanishing points detected in the image. To achieve the best performance, rate-distortion optimization is utilized to determine whether planar perspective transformation would be used in intra block copy for coding unit (CU) or not. In this way, we enable much more flexible prediction for both non-local translational and non-local perspective repeated image content. Experimental results show that our proposed method can achieve as the highest as 9.9% and averagely 2.9% bit-rate saving on the test images for urban building scenes compared to conventional intra block copy on HEVC platform.

Scalable video coding (SVC) is considered as a potentially promising solution to enable the adaptability of video to heterogonous networks and various devices. In spatially scalable video encoder, how to resize the captured high-resolution video to get low-resolution video has great effect on the quality of experience (QoE) in the clients receiving low-resolution video. In this paper, we propose a new resizing algorithm called hybrid epitomic resizing (HER), which can make the resized image preserve the same ‘physical’ resolution with original image by the way of utilizing texture similarity inside image and highlight regions of interest while avoiding potential artifacts. For hybrid epitomic resizing, we also design two new inter-layer prediction methods to eliminate the redundancy between adjacent spatial layers instead of conventional inter-layer prediction. Experimental results show that HER can get resized images with perceptually much better quality and the performance of new inter-layer prediction are comparable to that of conventional inter-layer prediction in H.264 SVC.