Perceptual image quality assessment by independent feature detector

The development of image processing technology has triggered the increasing demand for accurate methods of image quality assessment (IQA). Thus, creating reliable and accurate image quality metrics (IQMs) that are consistent with subjective human evaluation is an intense focus of research. Because the human visual system (HVS) is the ultimate receiver of images, modeling of the HVS has been regarded as the most suitable way to achieve perceptual quality predictions. In fact, independent component analysis (ICA) can provide a very good description for the receptive fields of neurons in the primary visual cortex which is the most important part of the HVS. Inspired by this fact, a novel independent feature similarity (IFS) index is proposed for full-reference IQA. Moreover, ICA can simulate the color-opponent mechanism of the HVS. Thus IFS can effectively predict the quality of an image with color distortion. Because IFS uses only a part of the reference image information, it can also be considered as a reduced-reference IQM. The proposed method is based on independent features that are acquired from a feature detector which is trained on samples of natural images by ICA. The computation of IFS consists of two components: feature component and luminance component. The feature component measures the structure and texture differences between two images, while the luminance component evaluates brightness distortions. Experimental results show that IFS has relatively low computational complexity and high correlation with subjective quality evaluation.