Blind video quality assessment based on Spatio-Temporal Feature Resolver

Abstract

As a method to measure video quality, blind video quality assessment (BVQA) plays an important role in video related applications. Feature extraction, as a vital component of BVQA, significantly impacts the performance and speed of the method. In the process of designing BVQA method, we make the feature extraction process independent of the whole quality evaluation method. First, a Spatio-Temporal Feature Resolver (STFR) is obtained by training. Then, the STFR is employed to directly extract the spatio-temporal features of the video sequences. Finally, the extracted features are mapped to quality scores using Support Vector Regression (SVR). STFR only needs to be trained in an undistorted video sequence, and can be directly applied to video sequences of various scenes, which has a universality. To evaluate the effectiveness of the proposed method, the experimental results on four published video quality databases show that the proposed BVQA method not only achieves more accurately than the existing BVQA methods, but also exhibits significant competitiveness in computational speed.