Efficient Computational Cost Saving in Video Processing for QoE Estimation

Abstract

No-Reference video quality assessment has become a trending and challenging hot topic in estimating perceived quality in audiovisual content. In this paper, we present a proposal to considerably reduce the computational cost of video processing without losing accuracy in QoE estimation. Tests have been performed using the Video-MOS SaaS solution, a hybrid NR-VQA solution based on perceptible video distortions and a machine learning approach. After exploring the spatial and temporal redundancy present in a video sequence, the final approach combines video metric feature extraction in both high and low video resolution, together with a specific frame selection based on a uniform temporal sampling and frame type at the video coding level. An extensive validation with more than 144 hours of audiovisual content from six of the most important HD channels of DTT in Spain demonstrates the validity of the approach, ensuring real-time application on the test device, with computational cost savings of 94.96% and an obtained MOS error of 0.1144, in more than 174000 3-second measurements.INDEX TERMS Computational cost, feature extraction, I frames, machine learning, Mean Opinion Score (MOS), no-reference, video quality assessment, perceived quality, Quality of Experience (QoE), video processing.