In immersive media communication with point cloud (PC), PC compression will inevitably cause distortion, which may affect quality of users’ visual experience. To monitor the visual quality of PCs, point cloud quality assessment (PCQA) metrics are highly desired. Inspired by the video-based point cloud compression (V-PCC) standard to project a PC to planes and quantify the corresponding projection maps, a new blind PCQA metric based on V-PCC texture projection map and geometry projection map of PC is proposed for evaluating PC with compression distortion. Specifically, considering the combination of texture and geometry features of the projection maps to characterize distorted PC, a dual-stream convolutional network is designed from the perspective of global and local feature description to extract texture and geometry features of the distorted PC. The network mainly consists of two modules, that is, global texture and geometry fusion (GTGF) module and local geometry weighted local texture (LGWLT) module. The GTGF module is designed to realize the fusion of global texture and geometry features, which can describe PC's combination effect of texture and geometry distortions. Aiming at the relationship between texture and geometry distortions, the LGWLT module is designed to extract the features of local texture distortion caused by local geometry distortion. Furthermore, visual perception is combined to take distortions of the PC's salient regions into account in the process of PCQA. The experimental results on three PC databases show that the proposed PCQA metric is more consistent with visual perception, thus has greater potential than the representative PCQA metrics.

High Efficiency Video Coding (HEVC) can significantly improve the compression efficiency in comparison with the preceding H.264/Advanced Video Coding (AVC) but at the cost of extremely high computational complexity. Hence, it is challenging to realize live video applications on low-delay and power-constrained devices, such as the smart mobile devices. In this paper, we propose an online learning-based multi-stage complexity control method for live video coding. The proposed method consists of three stages: multi-accuracy Coding Unit (CU) decision, multi-stage complexity allocation, and Coding Tree Unit (CTU) level complexity control. Consequently, the encoding complexity can be accurately controlled to correspond with the computing capability of the video-capable device by replacing the traditional brute-force search with the proposed algorithm, which properly determines the optimal CU size. Specifically, the multi-accuracy CU decision model is obtained by an online learning approach to accommodate the different characteristics of input videos. In addition, multi-stage complexity allocation is implemented to reasonably allocate the complexity budgets to each coding level. In order to achieve a good trade-off between complexity control and rate distortion (RD) performance, the CTU-level complexity control is proposed to select the optimal accuracy of the CU decision model. The experimental results show that the proposed algorithm can accurately control the coding complexity from 100% to 40%. Furthermore, the proposed algorithm outperforms the state-of-the-art algorithms in terms of both accuracy of complexity control and RD performance.

Tone-mapping technology aims to transform high dynamic range (HDR) images into standard dynamic range images for visualizing on standard screens. However, various visual artifacts will inevitably be induced during the tone-mapping process. To quantify the visual quality degradation for tone-mapped images (TMIs) accurately and automatically, a robust blind TMI quality measurement method is developed by the analysis of low-level and high-level perceptual characteristics. Specifically, for low-level visual features, considering that the tone-mapping operator is prone to destroy the local image contrast, which conveys vital structural information, we resort to the joint statistical features based on gradient map and Laplacian of Gaussian response to portray the local image contrast variation in TMIs. In addition, chromatic local binary pattern (CLBP) is leveraged to measure the colorfulness degradation over four chromatic descriptor maps. For high-level quality-aware features, deep-learned features are utilized by a pretrained convolutional neural network to characterize the semantics variation. Finally, the extracted low-level and high-level features are combined and mapped into an overall quality score by regression function. Extensive experiments show that our metric achieves a higher performance over two publicly available TMI benchmark databases.

Screen content coding (SCC) significantly improves the screen content compression efficiency over the high efficient video coding but at the cost of extremely huge computational complexity. The flexible quad-tree coding unit (CU) partitioning structure and the newly-introduced SCC intra modes are largely responsible for the high computational complexity. In order to meet the challenge of huge computational complexity of SCC, we propose an efficient CU and prediction unit (PU) decision based on neural network (NN) and gray level co-occurrence matrix (GLCM) for SCC intra prediction. The proposed efficient SCC intra prediction algorithm contains three stages, including NN-based CU classification model, efficient PU mode decision based on classification (EPMD), and efficient CU size decision based on GLCM and spatiotemporal correlations (ECSD). Consequently, the computational complexity of SCC intra prediction can be drastically reduced by replacing the brute-force search with EPMD and ECSD to decide the optimal combination of CU size and PU mode. In addition, an online updating method of weighted factor is introduced to cope with different characteristics of test sequences. In order to achieve a good tradeoff between complexity reduction and rate distortion (RD) performance, extensive experiments are conducted to select the optimal threshold for ECSD. The experimental results show that in comparison with the original SCC reference software, the proposed algorithm can reduce 49.33% intra coding time with 1.36% BDBR increase and 0.13 dB BDPSNR decrease. Meanwhile, the proposed algorithm outperforms eight state-of-the-art algorithms in terms of computational complexity reduction and RD performance.