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Proceedings Paper

Information theoretical assessment of image gathering and coding for digital restoration
Author(s): Friedrich O. Huck; Sarah John; Stephen E. Reichenbach
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Paper Abstract

In this paper we are concerned with the end-to-end performance of image gathering, coding, and restoration as a whole rather than as 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 the information density and efficiency of the acquired signal as a function of the image-gathering system design and the radiance-field statistics, and for the 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 image gathering and coding for digital restoration. Foremost is the realization that images can be restored with better quality and from less data as the information efficiency of the transmitted 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, can be attained only by minimizing imagegathering degradations as well as signal redundancy. Another important realization is that the critical constraints imposed on both image gathering and natural vision limit the maximum acquired information density to ~ 4 binary information units (bifs). This information density requires ~ 5-bit encoding for transmission and recording when lateral inhibition is used to compress the dynamic range of the signal (irrelevancy reduction). This number of encoding levels is close (perhaps fortuitously) to the upper limit of the ~ 40 intensity levels that each nerve fiber can transmit, via pulses, from the retina to the visual cortex within ~l/20 sec to avoid prolonging reaction times. If the data are digitally restored as an image on film for ‘best’ visual quality, then the information density may often be reduced to ~3 bifs or even less, depending on the scene, without incurring perceptual degradations because of the practical limitations that are imposed on the restoration. These limitations are not likely to be found in the nervous system of human beings, so that the higher information density of ^4 bifs that the eye can acquire probably contributes effectively to the improvement in visual quality that we always experience when we view a scene directly rather than through the media of image gathering and restoration.

Paper Details

Date Published: 1 September 1990
PDF: 18 pages
Proc. SPIE 1360, Visual Communications and Image Processing '90: Fifth in a Series, (1 September 1990); doi: 10.1117/12.24173
Show Author Affiliations
Friedrich O. Huck, NASA/Langley Research Ctr. (United States)
Sarah John, Science and Technology Corp. (United States)
Stephen E. Reichenbach, Univ. of Nebraska (United States)

Published in SPIE Proceedings Vol. 1360:
Visual Communications and Image Processing '90: Fifth in a Series
Murat Kunt, Editor(s)

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