Saliency-based point cloud quality assessment method using aware features learning

This paper deals with a saliency-based no-reference (NR) method for 3D point cloud (PC) quality assessment. For this purpose, we firstly compute 3D visual saliency map for each distorted point cloud. Then, we use a threshold-based filter to select the most salient points. From these, we extract both geometrical a perceptual attributes. Estimates of their statistical properties (Entropy, Standard deviation, Skewness, Kurtosis, Median and Mean) form a features vector. In the end, the Support vector regressor (SVR) is utilized for the characteristics regression and the quality score prediction. To validate our method, a set of experiments are conducted on an open subjective colored point cloud dataset (SJTU-PCQA). Results show that the suggested method exceeds some competing methods accord-ina to correlation with average opinion score.