Towards a Point Cloud Quality Assessment Model using Local Binary Patterns

Abstract

The proliferation of devices such as mobile phones, virtual reality headsets, and head-mounted displays has increased the popularity of immersive applications that deliver realistic representations of the real world. Among the technologies that enable such applications, the point cloud (PC) technology seems to be one of the most mature alternatives, gaining prominence in academia, industry, and standardization committees. Although PC technologies have been used in entertainment, automotive, and geographical location industries, the design of objective quality assessment methods for PC contents is still an open problem. In this paper, we introduce a texture-based objective quality assessment method for PC contents. The method analyzes the texture of the PC content using the Local Binary Pattern (LBP) descriptor. Unlike points in still (2D) images, the points in a PC are not equally distributed in space. Therefore, we adapted the LBP descriptor to allow processing a PC point and its neighboring points. The statistics of the LBP outputs, for both reference and test PCs, are computed and compared to obtain a quality estimate for the test (impaired) PC content. Experimental results show that the proposed PC quality metric has a good correlation with subjective quality scores, outperforming state-of-the-art PC quality metrics.