Performance comparison of masking models based on a new psychovisual test method with natural scenery stimuli

Various image processing applications exploit a model of the human visual system (HVS). One element of HVS-models describes the masking-effect, which is typically parameterized by psycho-visual experiments that employ superimposed sinusoidal stimuli. Those stimuli are oversimplified with respect to real images and can capture only very elementary masking-effects. To overcome these limitations a new psycho-visual test method is proposed. It is based on natural scenery stimuli and operates in the wavelet domain. The collected psycho-visual data is finally used to evaluate the performance of various masking models under conditions as found in real image processing applications like compression.