Compressed video quality assessment with modified MSE

A method to adjust the mean-squared-errors (MSE) value for coded video quality assessment is investigated in this work by incorporating subjective human visual experience. First, we propose a linear model between the mean opinioin score (MOS) and a logarithmic function of the MSE value of coded video under a range of coding rates. This model is validated by experimental data. With further simplification, this model contains only one parameter to be determined by video characteristics. Next, we adopt a machine learing method to learn this parameter. Specifically, we select features to classify video content into groups, where videos in each group are more homoegeneous in their characteristics. Then, a proper model parameter can be trained and predicted within each video group. Experimental results on a coded video database are given to demonstrate the effectiveness of the proposed algorithm.