Learning-Based Quality Enhancement For Scalable Coded Video Over Packet Lossy Networks

Yu, Shengwei; Tong, Xun; Huang, Yan; Xie, Rong; Song, Li

doi:10.1109/icme46284.2020.9102764

Cited by 2 publications

(1 citation statement)

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“…According to the output of the classifier, points related to the visible side are retained, which in turn significantly improves the visual quality of the point cloud. As a significant amount of work has been devoted to optimize visual quality of either 2D video [14], scalable video [15] or point cloud, predicting perceived visual quality computationally is essential for the optimization and evaluation of compression algorithms. Simplest metrics like point-to-point distance, which evaluates quality of point cloud at point level, cannot reasonably evaluate visual quality of the reconstructed point cloud [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Visual Quality Optimization for View-Dependent Point Cloud Compression

Wang

Zhu

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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The video-based point cloud compression (V-PCC) is the state-of-the-art dynamic point cloud compression technique. V-PCC projects the 3D point cloud data patch by patch to its bounding box and organizes projected patches into a video frame, making full use of the well-developed video coding tools. Despite its high efficiency, cracks easily exist in the reconstructed point cloud in various viewing angles, which seriously degrades the visual quality. In this paper, we propose an efficient method to improve the visual quality of dynamic point cloud, especially for the main view from the content provider. The relationship between patches and views is exploited, and an algorithm intelligently reserving points that may be discarded in V-PCC is proposed. According to our subjective and perceptual objective evaluation experiments, compared with V-PCC, the overall visual quality of the reconstructed point could is evidently improved. In particular, cracks are mended with our proposed method. The Bjøntegaard delta bit-rate reduction of up to 3.1% is achieved with respect to Point Cloud Quality Metric (PCQM), which partially verifies the improvement of subjective quality when adopting the proposed method.

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Section: Introductionmentioning

confidence: 99%