Panoramic images are critical for immersive VR/AR and 6DoF yet degraded by compression artifacts, projection distortion, and uneven sampling, with existing hybrid CNN-Transformer models struggling to reconcile fine details and structural consistency in panoramas; to address this, we propose Dynamic Channel Collaboration (DCC-Former) for panoramic enhancement, inspired by primate vision's hierarchical processing and three strategies: strengthening local feature representation via reparameterization and gating, enhancing global context with adaptive self-attention, and enabling cross-scale aggregation through cascaded multi-scale fusion, aligned with biological vision's ventral-dorsal stream division and fovea-periphery resource allocation to balance detail preservation, global consistency, and computational efficiency-extensive experiments on benchmark datasets demonstrate DCC-Former outperforms SOTA in restoration quality and inference efficiency, providing a practical-efficient paradigm for high-resolution panoramic enhancement.

This paper proposes a novel 3D neural network for RGB-D image compression, which treats depth as a temporal dimension of RGB and leverages 3D convolutions for unified cross-modal modeling. Specifically, the network employs a parallel Hybrid CNN and Gated SwinT-Mamba (HCGSM) module to jointly capture multi-scale local details and long-range global dependencies. Furthermore, it introduces a channel-wise autoregressive entropy model enhanced with a lightweight Feature Enhanced Attention (FEAtten) module to improve latent representation coding efficiency and preserve structural fidelity.

This paper proposes an image compression model based on high-low frequency decomposition and structural priors, as illustrated in Fig. 1. Specifically, we first employ Discrete Wavelet Transform (DWT) to decompose the image into high- and low-frequency components. Simultaneously, a Structural Prior Feature Extraction (SPFE) module is adopted to extract inherent structural information from medical images, which is incorporated into both the encoder and the entropy model at multiple scales as prior knowledge. Additionally, we design a Grayscale Feature Extraction (GFE) module by performing directional pooling and feature fusion on the image to further capture the gradually varying intensity characteristics commonly present in medical images.

The diversity of video content and unpredictability of distortions in user-generated content (UGC) videos pose a challenge for video quality assessment (VQA). Most existing methods are difficult to model complete visual perception loop to accurately capture video content and predict perceived quality. Thus, as shown in Figure 1 , this paper proposes a bidirectional hierarchical semantic extraction structure for VQA (BHSE-VQA), which simulates visual feedforward and feedback perception processes. Firstly, the feedforward and feedback multi-level network (FFMNet) based on the reverse hierarchy theory is designed to extract and adjust hierarchical semantic features on the bidirectional pathway. Then, considering the different effects of feature depth on visual perception results, this paper suggests a multi-level weight redistribution (MWR) strategy that makes use of the attention characteristics of the feedback outputs to realign the weights at each stage of the feedforward outputs. Finally, through a temporal attention fusion network (TAFNet), this paper further extracts the detailed features arising from feedforward and feedback perceptual differences and obtains quality scores. The experimental results show that the proposed model exceeds 0.845 on both SRCC and PLCC on YouTube-UGC database.

In the latest video coding standard, Versatile Video Coding (H.266/VVC), a new quadtree with nested multi-type tree (QTMTT) coding block structure is proposed. QTMTT significantly improves coding performance, but more complex block partitioning structure brings greater computational burden. To solve this problem, a fast intra block partition pattern pruning algorithm is proposed using gray level co-occurrence matrix (GLCM) to calculate texture direction information of coding units, terminating the horizontal or vertical split of the binary tree and the ternary tree in advance. Experimental results show that the proposed algorithm achieves up to 53.57% encoding time saving on average with negligible quality loss under all-intra conguration.

In order to achieve highly compact representation for videos, we propose an adaptive quantization based perceptual video coding (PVC) scheme in this paper. Because human only perceive the limited discrete-scale quality levels, the perceptual quantization is transformed into the problem of how to determine the maximum quantization parameter (Qp) under the same perceptual quality level. So, the relationship between perceptual quality level and quantization parameter is analyzed with the statistical way in this paper. The frame-level Qp value for each quality level is determined based on the maximum probability criterion. Then, the just noticeable distortion is estimated to guide the Qp adjustment in the coding unit level (CU-level). In summary, the perceptual quantization is achieved in both the frame level and the CU level according to characteristics of human visual system (HVS). Experimental results show that the proposed PVC scheme achieves substantial bitrate reduction with better subjective and objective quality in comparison with other PVC schemes.

The complexity of 3D-HEVC is fairly high due to quad tree structure and traversal searching in depth intra coding. In order to reduce complexity caused by coding unit (CU) size decision in rate distortion optimization (RDO) process, a fast algorithm based on adaptive QP convolutional neural network (AQ-CNN) structure is proposed in this paper. For each size of CU, the proposed structure automatically extracts deep feature information to terminate CU partition early. Specially, the AQ-CNN structure is suitable for different QPs because the QP has a great influence on CU partition and is connected into the CNN structure appropriately. Benefiting from the accurate prediction of CU partition label, the proposed algorithm reduces coding complexity sharply. Experimental results show that the proposed algorithm reduces the depth coding time by 69.4% with negligible BD-rate increase, and outperforms other recent algorithms in 3D-HEVC.

Multi-view texture video coding is very important, we propose a bit allocation method based on view layer and a bitrate decision method for P-frame of the dependent view (DV). First of all, considering that the distortion in the base view (BV) is directly transmitted to the DV by inter-view skip mode, the RD model of the DV is improved based on the inter view dependency. In this paper, a precise power model is derived based on our joint RD model to represent the target bitrates relationship between the BV and the DV. Then, since the P frame in the DV (P-DV) is mainly predicted from the corresponding I frame in the BV (I-BV) by inter-view prediction, the constant proportional relationship between the P-DV and the I-BV is discovered in this paper. Based on this discovery, a novel linear model is built to assign the target bitrates of the P-DV. Extensive experimental results exhibit that the proposed scheme provides a better RD performance than the state-of-the-art algorithms.

Image stitching technology has been widely used in immersive applications, such as 3D modeling, VR and AR. The quality of stitching results is crucial. At present, the objective quality assessment methods of stitched images are mainly based on the availability of ground truth (i.e., Full-Reference). However, in most cases, ground truth is unavailable. In this paper, a no-reference quality assessment metric specifically designed for stitched images is proposed. We first find out the corresponding parts of source images in the stitched image. Then, the isolated points and the outer points generated by spherical projection are eliminated. After that, we take advantage of the bounding rectangle of stitching seams to locate the position of overlapping regions in the stitched image. Finally, the assessment of overlapping regions is taken as the final scoring result. Extensive experiments have shown that our scores are consistent with human vision. Even for the nuances that cannot be distinguished by human eyes, our proposed metric is also effective.

3D video coding extension of High Efficiency Video Coding (3D-HEVC) adopts many high complexity approaches to improve the coding performance of the depth video, which leads to heavy computation. To alleviate the computation burden, a fast intra coding scheme for the depth map coding is proposed based on texture feature and spatial correlation. The coding unit (CU) sizes and intra prediction modes are selected differently due to different depth features. The CU block is divided into smooth block, texture block and edge block by using gray-level co-occurrence matrix (GLCM) and sobel operator. Firstly, for CU level, an early termination strategy of CU splitting for smooth CU is proposed to filter out unnecessary coding blocks. Then, for PU level, a fast candidate mode decision method (FCMDM) is proposed to reduce the redundant candidate modes based on PU's types and the prediction mode of neighbor PUs. Finally, a fast wedgelet pattern determination method based on K-Means is explored to reduce the complexity of depth modelling mode 1 (DMM1). Experimental results show that the proposed algorithm achieves an average time reduction of 40.71% for depth intra coding, with negligible drop in encoding quality. The proposed algorithm achieves better the time saving and coding performance compared with the state-of-the-art method.