Reduced-Reference Quality Assessment of Point Clouds via Content-Oriented Saliency Projection

Abstract

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 user-ends 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 experimental 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.

References in corpus (5)

• Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression
• No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models
• GraphIQA: Learning Distortion Graph Representations for Blind Image Quality Assessment
• Point Cloud Rendering after Coding: Impacts on Subjective and Objective Quality
• Blind Quality Assessment of 3D Dense Point Clouds with Structure Guided Resampling

Cited by in corpus (1)

• The Worse The Better: Content-Aware Viewpoint Generation Network for Projection-related Point Cloud Quality Assessment