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Reduced-reference quality assessment of point clouds via content-oriented saliency projection

Zhou, Wei, Yue, Guanghui, Zhang, Ruizeng, Qin, Yipeng ORCID: and Liu, Hantao ORCID: 2023. Reduced-reference quality assessment of point clouds via content-oriented saliency projection. IEEE Signal Processing Letters 30 , pp. 354-358. 10.1109/LSP.2023.3264105

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Many dense 3D point clouds have been exploited to represent visual objects instead of traditional images or videos. To evaluate the perceptual quality of various point clouds, in this letter, we propose a novel and efficient Reduced-Reference quality metric for point clouds, which is based on Content-oriented sAliency Projection (RR-CAP). Specifically, we make the first attempt to simplify reference and distorted point clouds into projected saliency maps with a downsampling operation. Through this process, we tackle the issue of transmitting large-volume original point clouds to end-users for quality assessment. Then, motivated by the characteristics of the human visual system (HVS), the objective quality scores of distorted point clouds are produced by combining content-oriented similarity and statistical correlation measurements. Finally, extensive experiments are conducted on SJTU-PCQA and WPC databases. The experiment results demonstrate that our proposed algorithm outperforms existing reduced-reference and no-reference quality metrics, and significantly reduces the performance gap between state-of-the-art full-reference quality assessment methods. In addition, we show the performance variation of each proposed technical component by ablation tests.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1070-9908
Date of First Compliant Deposit: 4 April 2023
Date of Acceptance: 25 March 2023
Last Modified: 22 Mar 2024 18:32

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