A No-Reference Bitstream-based Perceptual Model for Video Quality Estimation of Videos Affected by Coding Artifacts and Packet Losses

Abstract

In this work, we propose a No-Reference (NR) bitstream-based model for predicting the quality of H.264/AVC video sequences, aeffected by both compression artifacts and transmission impairments. The concept of the article is based on a feature extraction procedure, where a large number of features are calculated from the impaired bitstream. Many of the features are mostly proposed in this work, while the specificc set of the features as a whole is applied for the first time for making NR video quality predictions. All feature observations are taken as input to the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. LASSO indicates the most important features, and using only them, it is able to estimate the Mean Opinion Score (MOS) with high accuracy. Indicatively, we point out that only 13 features are able to produce a Pearson Correlation Coefficient of 0:92 with the MOS. Interestingly, the performance statistics we computed in order to assess our method for predicting the Structural Similarity Index and the Video Quality Metric are equally good. Thus, the obtained experimental results verifi ed the suitability of the features selected by LASSO as well as the ability of LASSO in making accurate predictions through sparse modeling