Augmenting Blind Image Quality Assessment using Image Semantics

Blind image quality assessment aims to predict the perceptual quality of a distorted image without using information from its pristine version. So far, quality prediction has mostly been approached by modeling processes underlying human sensitivity to visual impairments, assuming quality to be depending on impairment visibility only. This assumption is limiting, as it does not adopt a holistic view of image viewing experience, which involves understanding and interpreting of content, in addition to perception. We propose to integrate blind image quality metrics with semantic information to account for the relationship between impairments and content recognition and understanding in image viewing experience. We first report on a subjective study that shows that image semantics influences perceptual quality. We then integrate several existing blind image quality metrics with semantic information, and show that this brings significant improvement in their accuracy in predicting perceptual quality.