Quantifying the visual quality of wavelet-compressed images based on local contrast, visual masking, and global precedence
The paper presents a two-stage metric which quantifies the visual quality of images that have undergone wavelet-based compression. The first stage operates via a model of visual pattern masking, which takes as input original and distorted images, and which outputs masked contrast detection thresholds. For distortions beyond the threshold of detection, the images and the thresholds are fed into a second stage which estimates visual quality based on the distance between the distribution of assumed ideal and actual contrast signal-to-noise ratios across scale-space. Results indicate that the proposed metric yields a higher correlation with subjective-rating data than other visual quality metrics when applied to a sample of wavelet-coded images (with rates ranging from approximately 0.08-0.85 bits/pixel) for which peak signal-to-noise ratio correlates poorly with subjective quality.