The perceptual relevance of scale-space image coding

Abstract In our research on image coding algorithms we have adopted the following starting points. First, processing by coding algorithms should as close as possible match what we know about the human visual system. Second, due to the lack of acceptable objective criteria, proper evaluation of coding algorithms, as well as parameter settings, require perceptual experiments. In this paper we summarize the so-called scale-space model and describe its application to image coding. In the scale-space model an image is passed through Gaussian filters of decreasing bandwidth. The variation between successively filtered responses is very systematic, so that little information is needed to pass between them. Starting from a low resolution version of the original image, we make a prediction for a higher resolution version. Only the prediction errors need be transmitted to recover this higher resolution picture. The process is repeated at a number of resolutions (called scales) in order to arrive at the original image. For data-reduction purposes, several approximations of these prediction errors can be studied. Evaluation of the resulting coded images is done by means of perceptual experiments. It is also shown in this paper that a one-to-one correspondence can be established between the different stages of the scale-space coder and a well-known model of the human visual system that is based on psychophysical data.