Perceptual coding of images

The problem of image compression is to achieve a low bit rate in the digital representation of an input image or video signal with minimum perceived loss of picture quality. Since the ultimate criterion of quality is that judged or measured by the human receiver, it is important that the compression (or coding) algorithm minimizes perceptually meaningful measures of signal distortion, rather than more traditional and tractable criteria such as the mean squared difference between the waveform at the input and output of the coding system. This paper develops the notion of perceptual coding based on the concept of distortion-masking by the signal being compressed, and describes how the field has progressed as a result of advances in classical coding theory, modelling of human vision, and digital signal processing. We propose that fundamental limits in the science can be expressed by the semi-quantitative concepts of perceptual entropy and the perceptual distortion-rate function, and we examine current compression technology with respect to that framework. We conclude with a summary of future challenges and research directions.