Analyzing the applicability of psychometric QoE modeling for projection-based point cloud video quality assessment

Abstract

Point cloud video delivery will be an important part of future immersive multimedia. In it, objects represented as sets of points are embedded within a video which is streamed and displayed to remote users. This opens possibilities towards remote presence scenarios such as tele-conferencing, remote education and virtual training. Due to its infeasibly high bandwidth requirements, encoding is unavoidable. The introduced artifacts and network degradations can have an important but unpredictable impact on the end-user’s Quality of Experience (QoE). Thus, real-time quality monitoring and prediction mechanisms are key to allow for fast countermeasures in case of QoE decrease. Since current state-of-the-art research is focusing on either continuous QoE monitoring of traditional video streaming services or objective delivery optimizations of point cloud content without any QoE validation, we believe this work brings a valuable contribution to current literature. Therefore, we present a no-reference (NR) QoE model, consisting of KMeans clustering and sigmoidal mapping, that works on video-level, group-of-pictures (GOP)-level and frame-level granularity. Results show the value of the sigmoidal mapping across all granularity levels. The clustering algorithm shows its value at the video-level and in the role of an outlier detector on the more fine-grained levels. Satisfying results are yet obtained with correlation values often going above 0.700 on GOP- and frame-level while maintaining root mean squared error (RMSE) below 10 on a 0–100 scale. In addition, a Command Line Interface (CLI) Video Metric Tool is presented that allows for easy and modular calculation of NR metrics on a given video.