Sparse correlation coefficient for objective image quality assessment

Image quality assessment (IQA) is of fundamental importance to numerous image processing applications. Generally, image quality metrics (IQMs) regard image quality as fidelity or similarity with a reference image in some perceptual space. Such a full-reference IQA method is a kind of comparison that involves measuring the similarity or difference between two signals in a perceptually meaningful way. Modeling of the human visual system (HVS) has been regarded as the most suitable way to achieve perceptual quality predictions. In fact, natural image statistics can be an effective approach to simulate the HVS, since statistical models of natural images reveal some important response properties of the HVS. A useful statistical model of natural images is sparse coding, which is equivalent to independent component analysis (ICA). It provides a very good description of the receptive fields of simple cells in the primary visual cortex. Therefore, such a statistical model can be used to simulate the visual processing at the level of the visual cortex when designing IQMs. In this paper, we propose a fidelity criterion for IQA that relates image quality with the correlation between a reference and a distorted image in the form of sparse code. The proposed visual signal fidelity metric, which is called sparse correlation coefficient (SCC), is motivated by the need to capture the correlation between two sets of outputs from a sparse model of simple cell receptive fields. The SCC represents the correlation between two visual signals of images in a cortical visual space. The experimental results after both polynomial and logistic regression demonstrate that SCC is superior to recent state-of-the-art IQMs both in single-distortion and cross-distortion tests.

After training on the reference image data Xref, the elements in the simple cell matrix W are very similar to the receptive fields of simple cells in the primary visual cortex. W can be seemed as a linear transform between a real space and a cortical visual space. Then image quality scores can be obtained by calculating the correlation coefficients between the two sets of outputs (Sref and Sdis).Figure optionsDownload as PowerPoint slideHighlights
► A novel image quality metric named sparse correlation coefficient (SCC) is proposed for full-reference quality assessment.
► Statistical models of natural images are used to simulate the visual processing of the human visual system.
► SCC can provide accurate and stable results for different distortions.
► Moreover, it is very effective in evaluating images with Gaussian blur or Fast fading distortion.