Reduced Reference Image Quality Assessment Based on Statistics of Edge

Objective Image Quality Assessment (IQA) model investigation is a hot topic in recent times. This paper proposed a novel and efficient universal Reduced Reference (RR) image quality assessment method based upon the statistics of edge discrimination. Firstly, binary edge maps created from the multi-scale wavelet transform modulus maxima were used as the low level feature to discriminate the difference between the reference and distorted image for IQA purpose. Then the gradient operator was applied on the binary map to produce the so called edge pattern map. The histogram of edge pattern map was used to verify the pattern of the edges of reference and distorted image, respectively. The RR features extracted from the histogram was used to discriminate the difference of edge pattern maps, and then form a new RR IQA model. Comparing to the typical RR model (Zhou Wang's method, 2005), only 12 features (96 bits) are needed instead of 18 features (162 bits) in Zhou Wang et al.'s method with better overall performance.