Learning the properties of Receptive Fields in the context of Perceptual Image Quality assessment

In this paper we introduce a statistical framework for image quality assessment based on the properties of hierarchical receptive fields (RFs) which are the primary mechanism for detection of visual patterns in the human visual system (HVS). We show how this frame work can be used to learn about different aspects of RFs such as the shape and size of RFs in the early vision and the directional preference of the RFs in the V1 cortex. The proposed framework offers a probabilistic approach to the detection of discrepancies (distortion) between a reference and a test visual stimuli (e.g. images). The proposed Probabilistic Perceptual Image Quality (PPIQ) framework offers a more realistic notion of image quality assessment, based on ldquocomparative memoryrdquo as opposed to ldquodifferential photographic memoryrdquo, which was required for explanation of many aspects of legacy image quality methods.