No abstract

Recently, point clouds have shown to be a promising way to represent 3D visual data for a wide range of immersive applications, from augmented reality to autonomous cars. Emerging imaging sensors have made easier to perform richer and denser point cloud acquisition, notably with millions of points, thus raising the need for efficient point cloud coding solutions. In such scenario, it is important to evaluate the impact and performance of several processing steps in a point cloud communication system, notably the quality degradations associated to point cloud coding solutions. Moreover, since point clouds are not directly visualized but rather processed with a rendering algorithm before shown on any display, the perceived quality of point cloud data highly depends on the rendering solution. In this context, the main objective of this paper is to study the impact of several coding and rendering solutions on the perceived user quality and in the performance of available objective quality assessment metrics. Another contribution regards the assessment of recent MPEG point cloud coding solutions for several popular rendering methods which was never presented before. The conclusions regard the visibility of three types of coding artifacts for the three considered rendering approaches as well as the strengths and weakness of objective quality metrics when point clouds are rendered after coding.

No abstract

Lossy image compression is a popular, simple and effective solution to reduce the amount of data representing digital pictures. In most lossy compression methods, the reduced volume of data in bits is achieved at the expense of introducing visual artifacts in the picture. The perceptual quality impact of such artifacts can be assessed with expensive and timeconsuming subjective image quality experiments or through objective image quality metrics. However, the faster and less resource demanding objective quality metrics are not always able to reliably predict the quality as perceived by human observers. In this paper, the performance of 14 objective image quality metrics is benchmarked against a dataset of compressed images labeled with their subjective quality scores. Moreover, the performance of the above objective quality metrics in predicting the subjective quality of images distorted by both conventional and learning-based lossy compression artifacts is assessed and conclusions are drawn.