Bitstream-based Perceptual Quality Assessment of Compressed 3D Point Clouds

With the increasing demand of compressing and streaming 3D point clouds under constrained bandwidth, it has become ever more important to accurately and efficiently determine the quality of compressed point clouds, so as to assess and optimize the quality-of-experience (QoE) of end users. Here we ma...

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Publié dans:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - PP(2023) vom: 07. März
Auteur principal: Su, Honglei (Auteur)
Autres auteurs: Liu, Qi, Liu, Yuxin, Yuan, Hui, Yang, Huan, Pan, Zhenkuan, Wang, Zhou
Format: Article en ligne
Langue:English
Publié: 2023
Accès à la collection:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Sujets:Journal Article
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520 |a With the increasing demand of compressing and streaming 3D point clouds under constrained bandwidth, it has become ever more important to accurately and efficiently determine the quality of compressed point clouds, so as to assess and optimize the quality-of-experience (QoE) of end users. Here we make one of the first attempts developing a bitstream-based no-reference (NR) model for perceptual quality assessment of point clouds without resorting to full decoding of the compressed data stream. Specifically, we first establish a relationship between texture complexity and the bitrate and texture quantization parameters based on an empirical rate-distortion model. We then construct a texture distortion assessment model upon texture complexity and quantization parameters. By combining this texture distortion model with a geometric distortion model derived from Trisoup geometry encoding parameters, we obtain an overall bitstream-based NR point cloud quality model named streamPCQ. Experimental results show that the proposed streamPCQ model demonstrates highly competitive performance when compared with existing classic full-reference (FR) and reduced-reference (RR) point cloud quality assessment methods with a fraction of computational cost 
650 4 |a Journal Article 
700 1 |a Liu, Qi  |e verfasserin  |4 aut 
700 1 |a Liu, Yuxin  |e verfasserin  |4 aut 
700 1 |a Yuan, Hui  |e verfasserin  |4 aut 
700 1 |a Yang, Huan  |e verfasserin  |4 aut 
700 1 |a Pan, Zhenkuan  |e verfasserin  |4 aut 
700 1 |a Wang, Zhou  |e verfasserin  |4 aut 
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