Perceptual Image Distortion

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.

The leftmost image below is the original image. The two images to the right of it were created by adding different types of distortions to the original image. The root mean squared error (RMSE) between the first distorted image and the original is smaller than the root mean squared error between the second distorted image and the original. In spite of that, the distortion is more visible in the left image.

The images directly below each distorted image are the predictions by the perceptual image distortion model. Lighter areas indicate regions where the distortion is more visible while darker areas indicate regions where the distortion is less visible. The model correctly predicts that the first distorted image is more visibly distorted than the second.

To further validate the model's performance, we applied the model to JPEG compressed images. The original image was compressed using the JPEG algorithm at different quality settings. The model was then used to predict the visibility of the distortion between each compressed image and the original.

The vertically-stacked pairs of images below are images compressed using the JPEG algorithm along with the model's predictions of the amount of visible distortion when compared with the original.

The image compressed at a quality setting of 80 is virtually indistinguishable from the original. The model's prediction corroborates this observation. The average distortion value computed by the model is 1.2, which indicates that the distortion is slightly above threshold (threshold is set at 1.0). The image compressed at a quality setting of 20 is slightly deteriorated while the image compressed at a quality setting of 10 shows marked blocking artifacts. The model's predictions agree with these trends fairly well.