Image quality evaluation based on recognition times for fast image browsing applications

Mean squared error (MSE) and peak signal-to-noise-ratio (PSNR) are the most common methods for measuring the quality of compressed images, despite the fact that their inadequacies have long been recognized. Quality for compressed still images is sometimes evaluated using human observers who provide subjective ratings of the images. Both SNR and subjective quality judgments, however, may be inappropriate for evaluating progressive compression methods which are to be used for fast browsing applications. In this paper, we present a novel experimental and statistical framework for comparing progressive coders. The comparisons use response time studies in which human observers view a series of progressive transmissions, and respond to questions about the images as they become recognizable. We describe the framework and use it to compare several well-known algorithms (JPEG, set partitioning in hierarchical trees (SPIHT), and embedded zerotree wavelet (EZW)), and to show that a multiresolution decoding is recognized faster than a single large-scale decoding. Our experiments also show that, for the particular algorithms used, at the same PSNR, global blurriness slows down recognition more than do localized "splotch" artifacts.