Visual perceptual entropy measure

Abstract

This paper is about a theoretical framework on perceptual reproduction quality assessment. — Visual perception is the main theme for two approaches on image metrics, visual performance and image quality. Visual performance looks at the perception of standard elementary images, such as gratings, by different observers or by a single observer in different visual conditions; image quality looks at the perception of variations of the same complex scene by a standard observer. — Visual performance, for which Barten proposed a model in 1999, is studying threshold responses in visual perception. For image quality, on the other hand, indexes are built which must evaluate responses to a variety of suprathreshold visual characteristics, attempting to give results as close as possible to human visual perception. — In audiovisual field, all the configurations approved by the creative team must be reproduced in the final viewing. This cannot be measured by visual performance or image quality indexes. A third approach is needed. This may be called perceptual reproduction quality assessment. Visual perception assessment is not measuring defects, per se; it is built to evaluate the perceptual effectiveness of a given scene coding through a given reproduction technology. — It is only dependent on the number of configurations being reproduced by a given theater system relatively to the various configurations reproduced and approved in the review room. As such, it is a statistical measure. It is in fact similar to the definition of relative entropy. One important fact is that this evaluation must include a model of visual perception. — This paper shows how relative entropy, and entropy loss, can be estimated while taking into account the coding transform, the projector performances, maximum light and the characteristics of human visual system. — This scheme is carried easily for monochromatic content. Color, trichromacy, is quite challenging. The size of configurations goes from 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12</sup> to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">36</sup> , around 6 billion configurations. Calculation and memory requirements are huge. Non-linearities are adding complexity leading to calculation of an upper limit only of entropy loss. — Presentation of results is also a challenge. One way to solve this is to process separately lightness and chromatic information. — In summary the main arguments in this paper are: — • Besides image quality and visual performance there is a need for perceptual reproduction quality assessment — • Entropy is the proper statistical estimator to evaluate the perceptual reproduction quality — • Color reproduction quality estimation requires processing lightness and chromatic attributes separately