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Abstract

An image processing system that automatically optimizes the perceptual quality of images undergoing a series of selected image-processing operations. The automatic optimization capability allows the system to bypass expensive and time-consuming trial-and-error methods associated with interactive methods. The system consists of a set of image-processing operations, an architecture, and an intelligent control. These elements take into consideration profiles of sources from which the images are generated, profiles of intended applications, and the impact that image processing operations (individually or in concert) will have on perceived image quality. The analysis uses a body of relationships linking human perception of image quality with objective metrics (such as sharpness, grain, tone, and color) of image content. The relationships used are based upon extensive psychovisual testing, using human observers and photographic images. The intelligent control embodies the test results and thereby functions as a synthetic human observer. Controlling a collection of adjustable parameters in the requested image-processing operations, it works automatically to maximize subjective quality for the resulting picture. When optimal parameter values have been determined, the system then subjects the image data, itself, to the corresponding processing operations and provides an image to the end application. This is all achieved without operator intervention other than initially selecting the set of processing operations. The functionality of the system may be implemented in a variety of physical architectures, including computer workstations, and including communicating sets of discrete components in which the computations and control are distributed among the components.

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