Towards a semantic-driven metric for image quality

We propose a pilot study in view of the definition of a semantic based absolute metric for automatic image quality evaluation. The basic idea is that the behavioral relevance of the objects present in the scene determines the extent of the impact of a given amount of (measurable) degradation on the perceived quality. Such an assumption is investigated following the top-down approach. The images are considered as collections of semantic units holding different relevance in the scene interpretation process. The test set is built such that some pre-defined categories of objects are present and the goal is to determine the impact of the selective degradation of instances of objects pertaining to the different categories on the perceived quality of the corresponding images. In particular, we focus on human faces. Under the assumption that human faces are salient cues from a perceptual point of view in an image quality evaluation task, we investigate the impact of the selective degradation of face/not-face regions of a pre-defined set of natural images through a subjective test performed by human observers. Our hypothesis is that the perceived quality is ruled by the nesses evaluated on human faces.