A novel image quality metric based on morphological component analysis

Due to that human eye has different perceptual characteristics for different morphological components, so a novel image quality metric is proposed by incorporating morphological component analysis (MCA) and human visual system (HVS), which is capable of assessing the image with different types of distortion. Firstly, reference and distorted images are decomposed into texture and cartoon components by MCA respectively. Then these components are changed into perceptual features by just noticeable difference (JND) which integrates masking features, luminance adaptation and contrast sensitive function (CSF). Finally, the difference between reference and distorted images´ perceptual features is quantified using a pooling strategy, and then the final result of the image quality is obtained. Experimental results demonstrate that the performance of the metric prevail over some existing methods on LIVE database II.