Exploiting user interactivity in quality assessment of point cloud imaging

Abstract

Point clouds are a new modality for representation of plenoptic content and a popular alternative to create immersive media. Despite recent progress in capture, display, storage, delivery and processing, the problem of a reliable approach to subjectively and objectively assess the quality of point clouds is still largely open. In this study, we extend the state of the art in projection-based objective quality assessment of point cloud imaging by investigating the impact of the number of viewpoints employed to assess the visual quality of a content, while discarding information that does not belong to the object under assessment, such as background color. Additionally, we propose assigning weights to the projected views based on interactivity information, obtained during subjective evaluation experiments. In the experiment that was conducted, human observers assessed a carefully selected collection of typical contents, subject to geometry and color degradations due to compression. The point cloud models were rendered using cubes as primitive elements with adaptive sizes based on local neighborhoods. Our results show that employing a larger number of projected views does not necessarily lead to better predictions of visual quality, while user interactivity information can improve the performance.

A Color-Based Objective Quality Metric for Point Cloud Contents

“…In order to directly exploit the vast body of research that has been done on image and video objective quality assessment, it has been proposed to project the 3D model onto a planar grid. In this case, a 2D representation of the volumetric content from a certain viewpoint would be obtained, and its visual quality can then be assessed using popular metrics, such as PSNR or VIF [8]. The method is usually praised for its ability to simultaneously capture both geometry and color degradations.…”

“…This approach has been used by Quiroz et al [7] to drive rate distortion optimization in their codec design. Alexiou et al [8] investigated the impact of the number of viewports on the performance of the metric, and proposed a weighting system based on user interaction. However, this approach is not suitable to be used in rate-distortion evaluation for lossy compression, as a large number of views would be needed in order to evaluate the geometry of the entire object.…”

A Reduced Reference Metric for Visual Quality Evaluation of Point Cloud Contents

“…The approach has been pioneered by Queiroz et al [13] to drive the rate-distortion optimization in their codec, and its performance has been analyzed in [14]. Alexiou et al [15] investigated the impact of the number of viewports on the performance of the metric, and proposed a weighting system based on user interaction.…”

PointXR: A Toolbox for Visualization and Subjective Evaluation of Point Clouds in Virtual Reality

“…noise) are additionally employed. No rendering [2], [4], [7], [11], rendering with fixed [5], [12] or adaptive point size [1], [9], [10], real-time voxelization [8] and conversion to mesh [3] have been proposed for display purposes.…”