Color and Geometry Texture Descriptors for Point-Cloud Quality Assessment

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Color and Geometry Texture Descriptors for Point-Cloud Quality Assessment

Rafael Diniz; Pedro Garcia Freitas; Mylène C. Q. Farias

Point Clouds (PCs) have recently been adopted as the preferred data structure for representing 3D visual contents. Examples of Point Cloud (PC) applications range from 3D representations of small objects up to large scenes, both still or dynamic in time. PC adoption triggered the development of new coding, transmission, and display methodologies that culminated in new international standards for PC compression. Along with these, in the last couple of years, novel methods have been developed for evaluating the visual quality of PC contents. This paper presents a new objective full-reference visual quality assessment metric for static PC contents, named BitDance, which uses color and geometry texture descriptors. The proposed method first extracts the statistics of color and geometry information of the reference and test PCs. Then, it compares the color and geometry statistics and combines them to estimate the perceived quality of the test PC. Using publicly available PC quality assessment datasets, we show that the proposed PC quality assessment metric performs very well when compared to state-of-the-art quality metrics. In particular, the method performs well for different types of PC datasets, including the ones where both geometry and color are not degraded with similar intensities. BitDance is a low complexity algorithm, with an optimized C++ source code that is available for download at .

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