No reference perceptual quality metrics: approaches and limitations

Abstract

To predict subjective quality it is necessary to develop and validate approaches that accurately predict video quality. For perceptual quality models, developers have implemented methods that utilise information from both the original and the processed signals (full reference and reduced reference methods). For many practical applications, no reference (NR) methods are required. It has been a major challenge for developers to produce no reference methods that attain the necessary predictive performance for the methods to be deployed by industry. In this paper, we present a comparison between no reference methods operating on either the decoded picture information alone or using a bit-stream / decoded picture hybrid analysis approach. Two NR models are introduced: one using decoded picture information only; the other using a hybrid approach. Validation data obtained from subjective quality tests are used to examine the predictive performance of both models. The strengths and limitations of the two NR methods are discussed.