Optimizing feature pooling and prediction models of VQA algorithms
In this paper, we propose a strategy to optimize feature pooling and prediction models of video quality assessment (VQA) algorithms with a much smaller number of parameters than methods based on machine learning, such as neural networks. Based on optimization, the proposed mapping strategy is composed of a global linear model for pooling extracted features, a simple linear model for local alignment in which local factors depend on source videos, and a non-linear model for quality calibration. Also, a reduced-reference VQA algorithm is proposed to predict the local factors from the source video. In the IRCCyN/IVC video database of content influence and the LIVE mobile video database, the performance of VQA algorithms is improved significantly by local alignment. The proposed mapping strategy with prediction of local factors outperforms one no-reference VQA metric and is comparable to one full-reference VQA metric. Thus predicting the local factors in local alignment based on video content will be a promising new approach for VQA.