Information theoretical assessment of digital imaging systems

In this paper we are concerned with the end-to-end performance of image gathering, coding, and restoration as a whole rather than a chain of independent tasks. Our approach evolves from the pivotal relationship that exists between the spectral information density of the transmitted signal and the restorability of images from this signal. The information theoretical assessment accounts for (1) the information density and efficiency of the acquired signal as a function of the image-gathering system design and the radiance-field statistics, and (2) the improvement in information efficiency and data compression that can be gained by combining image gathering with coding to reduce the signal redundancy and irrelevancy. The redundancy reduction is concerned mostly with the statistical properties of the acquired signal, and the irrelevancy reduction is concerned mostly with the visual properties of the scene and the restored image. The results of this assessment lead to intuitively appealing insights about the end-to-end performance of image gathering, coding, and restoration. Foremost is the realization that images can be restored with better quality and from less data as the information efficiency of the data is increased, providing that the restoration correctly accounts for the image gathering and coding processes and effectively suppresses the image-display degradations. High information efficiency, in turn, is attained by minimizing image-gathering degradations as well as signal redundancy. Further data compression can often be gained by matching the irrelevancy reduction to a specific restoration filter.