A novel no-reference image quality assessment metric based on statistical independence

No-reference image quality assessment (NR IQA) has wide applicability to many problems. This paper focuses on the mechanism of divisive normalization transform (DNT) which simulates the behavior of visual cortex neurons to extract the independent components of natural images, analyzes the difference between the statistics of neighboring DNT coefficients of the images of a variety of distortion, and proposes a novel solution for NR IQA metric design. We demonstrate that measuring the statistical independence between neighboring DNT coefficients could provide features useful for quality assessment. The performance of the proposed method is quite satisfactory when it was tested on the popular LIVE, CSIQ and TID2008 databases. The experimental results are fairly competitive with the existing NR IQA metrics.