3D Point-Cloud Quality Assessment Using Color and Geometry Texture Descriptors
Since the mid-20th century, the use of digital formats for visual content allowed a great evolution in how society communicates. The Internet and digital broadcast systems introduced in the decade 90 to the wider public allowed an incredible expansion of multimedia consumption by the people, while the telecommunication networks and providers were pushed to their limits to address the growing multimedia content demand. Older electronic imaging systems, notably TV broadcasting systems, were designed after long subjective quality analysis for the definition of parameters like the number of lines of the video. But recent digital visual content services need faster and more affordable ways of evaluating the human perceived quality of the always-evolving multimedia systems. To address the need for automatic quality assessment, in the past decades many visual quality models based on algorithms that run on digital computers have been proposed. While the existing models are remarkably advanced for 2D digital imagery, a new set of immersive media is dawning, with different data structures, to which the 2D methods are not applicable, and need novel quality assessment metrics. These novel dawning immersive media formats provide a 3D visual representation of real objects and scenes. In this new visual format, objects can be captured, compressed, transmitted, and visualized in real-time not anymore as a flat 2D image, but as 3D content, allowing free-viewpoint selection by a consumer of such media. One of the most popular formats for immersive media is Point Cloud (PC), which is composed of points with 3 geometry coordinates plus color information, and sometimes, other information like reflectance and transparency. This work presents a research on the quality assessment of 3D PC based on novel color and geometric texture statistics. Considering that distortions to both color and geometry attributes of 3D visual content affect the perceived visual quality, it is proposed in this work to use both color-based and geometry-based texture descriptors for PC to obtain the visual degradation through their statistics. This work introduces 4 novels PC texture descriptors, 3 of them color-based, while 1 is geometry-based. Also, a new voxelization method is proposed, which converts points to voxels (volume elements), and improves the performance of the color-based texture descriptors. The performance of the proposed PC quality assessment method is among the best of the state-of-the-art PC quality assessment methods while being flexible and extensible to adapt to different types of distortions.
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