Image quality assessment with lasso regression and pairwise score differences

Abstract

The reception of multimedia applications often depends on the quality of processed and displayed visual content. This is the main reason for the development of automatic image quality assessment (IQA) techniques which try to mimic properties of human visual system and produce objective scores for evaluated images. Most of them require a training step in which subjective scores, obtained in tests with human subjects, are used for parameters tuning. In this paper, it is shown that pairwise score differences (PSD) can be successfully used for training a full-reference hybrid IQA measure based on the least absolute shrinkage and selection operator (lasso) regression. The results of extensive experimental evaluation on four largest IQA benchmarks show that the proposed IQA technique is statistically better than its version trained using raw scores, and both approaches are statistically better than state-of-the-art full-reference IQA measures. They are also better than other hybrid approaches. In the paper, the evaluation protocol is extended with tests using PSD.