Institute for Data Analysis and Visualization

Conventional mean squared error based methods for objective image quality assessment are not well correlated with human evaluation. The design of better objective measures of quality has attracted a lot of attention and several image quality metrics based explicitly on the properties of the Human Visual System (HVS) have been proposed in recent years. However, only in a few cases has the performance of such metrics been demonstrated on real images. In accounting for visual masking, all these metrics assume that the multiple channels mediating visual perception are independent of each other. Recent neuroscience findings and psychophysical experiments have established that there is interaction across the channels and that such interactions are important for visual masking. In this work, we propose the Picture Distortion Metric (PDM) which integrates these new visual masking properties, and we evaluate its performance for image coding applications. We evaluate the performance at medium to high range of quality to predict subjective scores on a 0-10 numerical scale, and on a wide range of quality for the 1-5 CCIR impairment scale.