Point cloud (PC) compression inevitably introduces distortion during communication, which can affect users’ visual experience. Thus, efficient point cloud quality measurement (PCQM) tools are highly desired to measure the PC’s visual quality. In this paper, a pseudo-reference PCQM metric based on joint two-dimensional (2D) and three-dimensional (3D) distortion description is proposed. In 2D description, aiming at the visual quality degradation reflected cooperatively by PC’s texture distortion and geometry distortion, a joint texture-geometry distribution with texture projection map and geometry projection map of the video-based point cloud compression (V-PCC) standard is constructed to measure the joint texture-geometry distortion of PC. Since the geometry distortion of PC results in the similar distortion phenomena in the geometry projection map and texture projection map, a self-reference geometry-texture structural similarity (SGT-SSIM) is proposed. The separate statistical features of texture projection map and geometry projection map are also considered. In 3D description, considering the limitations of using full-reference metric and the difficulty of directly reflecting PC cracks and outliers only by the V-PCC projection, a pseudo-reference PC is constructed by performing Poisson surface reconstruction on the distorted PC. Then, the point-to-distribution is used to directly characterize pseudo-referenced geometry distortion, while gray level-gradient co-occurrence matrix based on key points of PC is constructed to measure the texture distortion. Finally, the features with joint 2D and 3D distortion description are combined to measure the PC visual quality more comprehensively. Experimental results on five PC datasets demonstrate that the proposed metric has comparable performance to the existing full-reference metrics.

The Journal of Electronic Imaging (JEI), copublished bimonthly with the Society for Imaging Science and Technology, publishes peer-reviewed papers that cover research and applications in all areas of electronic imaging science and technology.

To address the limitations in the efficiency of modality feature fusion in existing RGB-T semantic segmentation methods, which restrict segmentation performance, this paper proposes an edge-supervised attention-aware algorithm to enhance segmentation capabilities. Firstly, we design a feature fusion module incorporating channel and spatial attention mechanisms to achieve effective complementation and enhancement of RGB-T features. Secondly, we introduce an edge-aware refinement module that processes low-level modality features using global and local attention mechanisms, obtaining fine-grained feature information through element-wise multiplication. Building on this, we design a parallel structure of dilated convolutions to extract multi-scale detail information. Additionally, an EdgeHead is introduced after the edge-aware refinement module, with edge supervision applied to further enhance edge detail capture. Finally, the optimized fused features are fed into a decoder to complete the RGB-T semantic segmentation task. Experimental results demonstrate that our algorithm achieves mean Intersection over Union (mIoU) scores of 58.52% and 85.38% on the MFNet and PST900 datasets, respectively, significantly improving the accuracy of RGB-T semantic segmentation.

Deficiencies in road anomaly detection systems precipitate multifaceted risks, including elevated collision probabilities from unidentified hazards, compromised traffic flow efficiency, and exponential maintenance costs. Contemporary methods struggle with complex road environments, dynamic viewing perspectives, and limited datasets. We present AVD-YOLO, an enhanced YOLO variant that synergistically integrates Active Vision-Driven (AVD) multi-scale feature extraction with Position Modulated Attention (PMA) mechanisms. PMA addresses diminished target-background discriminability under variable illumination and weather conditions by capturing long range spatial dependencies, enhancing weak-feature target detection. The AVD technique mitigates missed detections caused by real-time viewing distance variations through adaptive multi-receptive field mechanisms, maintaining conceptual target fixation while dynamically adjusting feature scales. To address data scarcity, a comprehensive Multi-Class Road Anomaly Dataset (MCRAD) comprising 14,208 annotated images across nine anomaly categories is constructed. Experiments demonstrate that AVD-YOLO improves detection accuracy, achieving a 1.6% gain in mAP@0.5 and a 2.9% improvement in F1-score over baseline. These performance gains indicate both more precise localization of abnormal objects and a better balance between precision and recall, thereby enhancing the overall robustness of the detection model.

As a pivotal medium for immersive information, point clouds (PCs) inevitably suffer distortion during compression, impacting users’ visual experience. To enhance the visual quality of encoded PCs with very low bitrate G-PCC Octree, a hierarchical artifact removal method is proposed in this paper. In response to the computational complexity in restoring geometry distortion caused by the sparse retention of points during PC compression with very low bitrate G-PCC Octree encoding, octree segmentation is performed on PC to obtain multiple cubes for parallel processing, so as to enhance restoration efficiency. Considering the regularity of the voxelized grid-shaped loss caused by geometry distortion in encoded PCs, a method combining 3D U-Net with attention mechanism and upsampling operations is designed to ext distortion features. Moreover, accounting for the different widths of grid-shaped losses and varying degrees of point reduction induced by different encoding levels, the problem of geometry artifact removal is transformed into a predictive and classificatory challenge of confidence index for PC artifact removal. By partitioning restoration thresholds for different encoding levels, hierarchical artifact removal can be achieved to produce more accurate restoration results. Additionally, as color distortion partly originates from missing points caused by geometry distortion, a strategy involving color information mapping, interpolation and smoothing is presented to efficiently utilize the existing color information to achieve rapid color restoration. Experimental results on PCs from four datasets demonstrate that the proposed method shows advanced performance in both geometry and color artifact removal of distorted PCs with very low bitrate G-PCC Octree encoding.

As an emerging immersive media format, point clouds (PC) inevitably suffer from distortions such as compression and noise, where even local degradations may severely impair perceived visual quality and user experience. It is therefore essential to accurately evaluate the perceived quality of PC. In this paper, a no-reference point cloud quality assessment (PCQA) method that uses complexity-driven patch sampling and an attention-enhanced Swin-Transformer is proposed to accurately assess the perceived quality of PC. Given that projected PC maps effectively capture distortions and that the quality-related information density varies significantly across local patches, a complexity-driven patch sampling strategy is proposed. By quantifying patch complexity, regions with higher information density are preferentially sampled to enhance subsequent quality-sensitive feature representation. Given that the indistinguishable response strengths between key and redundant channels during feature extraction may dilute effective features, an Attention-Enhanced Swin-Transformer is proposed to adaptively reweight critical channels, thereby improving feature extraction performance. Given that traditional regression heads typically use a single-layer linear mapping, which overlooks the heterogeneous importance of information across channels, a gated regression head is designed to enable adaptive fusion of global and statistical features via a statistics-guided gating mechanism. Experiments on the SJTU-PCQA dataset demonstrate that the proposed method consistently outperforms representative PCQA methods.