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Characterization and Prediction of Image Quality

In the broadest sense, the benefits of modeling are at least threefold: (1) cycle time compression through reduced prototyping and experimentation; (2) identification of nonobvious optimal solutions that might be missed by empirical testing over a restricted range; and (3) education and training of practitioners through virtual experimentation. At Eastman Kodak Company, predictive modeling of image quality has proved to be of great value in all three regards and has been regularly used in formulating business strategies, guiding design decisions, establishing product aims, budgeting system tolerances, and benchmarking. This paper will provide an overview of selected topics pertaining to image quality modeling, including: (1) definition of numerical scales of image quality tied to physical standards; (2) design of low-noise judging techniques calibrated to these scales and suitable for untrained observers; (3) development of a method for combining results from single-attribute experiments to predict multivariate quality; (4) generation of objective metrics correlating with perceptual attributes of conventional and digital imaging systems; (5) construction of Monte Carlo models that predict image quality distributions based on imaging system specifications; and (6) verification of model predictions through independent expert assessment of images from prototype handout and customer intercept studies.