ES Postdoctoral Associate: Image Compression and the Evaluation of Quality

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9405058 Gray Lossy image compression methods, useful for cost and time efficiency of image storage and communication, imperfectly reconstruct the original image. Evaluating the resulting image quality is important and complex. Recent algorithms perform simultaneous compression and classification, presenting a further challenge for determining image quality. Quality is typically quantified subjectively by statistical analyses of viewer ratings, and objectively by signal-to-noise ratios, or by simulation of the actual use of the images. As gathering viewers scores of simulating actual use is an expensive and time-consuming endeavor, it is desirable to find computable measures of image quality that strongly correlate with or accurately predict the more expensive quality measures. Better computable distortion measures will permit better screening of candidate coding techniques and may provide improved code designs by incorporating them into the optimization algorithms used to design the codes. The research would seek to develop statistical methods to quantify the usefulness of computable measures of image quality in predicting the expensively obtained measures from human observers. The methods would be based on fitting general linear models, and evaluating significance using modern computer-intensive statistical sample reuse techniques. Variations on a variety of perceptually based computable measures and compare their predictive power will be implemented. These computable measures of distortion and statistical techniques could then be applied both for the evaluation of and design optimization of vector quantization algorithms for compression and joint compression/classification currently being developed. ***