We have developed a perceptual distortion measure based on a model of spatial pattern detection. It is important to recognize the relevance of these empirical spatial pattern detection results to developing measures of image integrity. In a typical spatial pattern detection experiment, the contrast of a visual stimulus (called the target) is adjusted until it is just barely detectable. Threshold contrasts of the target are measured over a range of spatial frequencies, mean luminances, and spatial extents. In some experiments (called contrast masking experiments), the target is also superimposed on a background pattern (called the masker). In other experiments (called luminance masking experiments), the target is superimposed on a brief, bright, uniform background. In either case (contrast or luminance masking), the contrast of the target is adjusted (while the masker is held fixed) until the target is just barely detectable. Typically, a target is harder to detect (i.e., a higher contrast is required) in the presence of a masker. A model that predicts spatial pattern detection is obviously useful in image processing applications. In the context of image compression, for example, the target takes the place of quantization error and the masker takes the place of the original image.
Our model consists of three main parts: a retinal component, a cortical component, and a detection mechanism. The retinal component is responsible for contrast sensitivity and its dependence on mean luminance masking. The cortical component accounts for contrast masking. To compute perceptual image distortion, the reference and distorted images are passed through these two stages of the model independently. At this point, the images have been normalized for the differential sensitivities of the human visual system. The final (detection mechanism) component of the model compares these two normalized images to give a measure of image fidelity. The final result is an image representing the probability of perceiving a distortion at each position in the distorted image.
Model predictions of visible distortion.
Distorted images with similar mean squared errors.