Abstract

Point cloud video streaming is a fundamental application of immersive multimedia. In it, objects represented as sets of points are streamed and displayed to remote users. Given the high bandwidth requirements of this content, small changes in the network and/or encoding can affect the users' perceived quality in unexpected manners. To tackle the degradation of the service as fast as possible, real-time Quality of Experience (QoE) assessment is needed. As subjective evaluations are not feasible in real time due to their inherent costs and duration, low-complexity objective quality assessment is a must. Traditional No-Reference (NR) objective metrics at client side are best suited to fulfill the task. However, they lack on accuracy to human perception. In this paper, we present a cluster-based objective NR QoE assessment model for point cloud video. By means of Machine Learning (ML)-based clustering and prediction techniques combined with NR pixel-based features (e.g., blur and noise), the model shows high correlations (up to a 0.977 Pearson Linear Correlation Coefficient (PLCC)) and low Root Mean Squared Error (RMSE) (down to 0.077 on a zero-to-one scale) towards objective benchmarks after evaluation on an adaptive streaming point cloud dataset consisting of sixteen source videos and 453 sequences in total.

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