A psychovisually tuned image codec

Abstract

A psychovisual quality driven image codec exploiting the psychological and neurological process of visual perception is proposed in this paper. Recent findings in brain theory and neuroscience suggest that visual perception is a process of fitting brain's internal generative model to the outside retina stimuli. And the psychovisual quality is related to how accurately visual sensory data can be explained by the internal generative model. Therefore, the design criterion of our psychovisually tuned image compression system is to find a compact description of the optimal generative model from the input image on the encoding end, which is then used to regenerate the output image on the decoding end. By noting an important finding from empirical natural image statistics that natural images have scale invariant features in the pixels' high order statistics, the generative model can be efficiently compressed through model preserving spatial downsampling on the encoder. And the decoder can reverse the process with a model preserving upsampling module to generate the decoded image. The proposed system is fully standard complaint because the downsampled image can be compressed with any exiting codec (JPEG2000 in this work). The proposed algorithm is shown to systematically outperform JPEG2000 in a wide bit rate range in terms of both subjective and objective qualities.