A NOVEL METRIC BASED ON MCA FOR IMAGE QUALITY

Abstract

Considering that the Human Visual System (HVS) has different perceptual characteristics for different morphological components, a novel image quality metric is proposed by incorporating Morphological Component Analysis (MCA) and HVS, which is capable of assessing the image with different kinds of distortion. Firstly, reference and distorted images are decomposed into linearly combined texture and cartoon components by MCA respectively. Then these components are turned into perceptual features by Just Noticeable Difference (JND) which integrates masking features, luminance adaptation and Contrast Sensitive Function (CSF). Finally, the discrimination between reference and distorted images perceptual features is quantified using a pooling strategy before the final image quality is obtained. Experimental results demonstrate that the performance of the proposed prevails over some existing methods on LIVE database II.