A complexity-based image analysis to investigate interference between distortions and image contents in image quality assessment

In this paper we investigate how distortion and image content interfere within image quality assessment. To this end we analyze how full reference metrics behave within three different groups of images. Given a dataset of images, these are first classified as high, medium or low complexity and the FR methods are applied within each group separately. We consider images from LIVE, CSIQ and LIVE multi-distorted databases. We evaluate 17 full reference quality metrics available in the literature on each of these the high, medium and low complexity groups. We observe that within these groups the metrics better correlate subjective data. In particular, the signal based metrics are the ones that show the highest improvements. Moreover for the LIVE multi-distorted database the gain in performance is evident for all the metrics considered.