Model-observer based quality measures for decompressed medical images

This paper provides the fundamental basis for model observers on decompressed images by the understanding of compression noise statistics. In medical applications, model observers have been successfully used to predict human observer performance and to empirically evaluate image quality for detection tasks on various backgrounds. To derive closed-form expressions for model observers, however, requires closed-form expressions for noise statistics. This paper views a decompressed image as the sum of the original image and compression noise. The statistics of compression noise depend on the compression algorithms. One of the most efficient image compression techniques is transform coding, on which the JPEG image compression standard is based. By analyzing transform coding, this paper derives probability density functions (PDF), and the first and second moments of compression noise. Those statistics are used to derive closed-form representations for the ideal and channelized Hotelling observers on decompressed images. It provides the closed-form decompressed image quality measurements in terms of model observer performance.