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

TL;DR

This paper introduces RR-CAP, a novel reduced-reference quality metric for 3D point clouds that simplifies reference and distorted data into saliency maps, effectively assessing perceptual quality while reducing data transmission needs.

Contribution

The paper presents the first content-oriented saliency projection method for reduced-reference point cloud quality assessment, improving efficiency and performance over existing metrics.

Findings

Outperforms existing reduced-reference and no-reference metrics.

Reduces the performance gap with full-reference methods.

Effective in practical quality assessment scenarios.

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.