A semi-reference image quality evaluation method based on Gabor difference box weighted dimension

Abstract

The invention discloses a semi-reference image quality evaluation method based on weighted dimension of Gabor difference box. Firstly, the input distorted image and the reference image are grayed andthen two-dimensional Gabor transform is performed, Gabor sinusoidal coefficient and cosine coefficient of that distorted gray image and the reference gray image are obtained, then the fractal dimension of Gabor sine coefficient and cosine coefficient of distorted gray image and reference gray image is calculated, difference box method is used to obtain the weighted dimension of difference box, Gabor entropy, sinusoidal weighted coefficient and cosine weighted coefficient of Gabor coefficients are calculated, and finally the semi-reference image quality evaluation score is calculated accordingto the weighted dimension and weighted coefficient of difference box. The image quality evaluation method of the invention overcomes the shortcomings that the dimension of the extracted image featureis too large to be transmitted conveniently, and the absolute difference value of the weighted dimension feature of the difference box is weighted by Gabor entropy to obtain the final objective imagequality evaluation score value, and the semi-reference image quality evaluation precision is improved.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a semi-reference image quality evaluation method based on Gabor difference box weighted dimensions. Background technique [0002] Image quality evaluation is to judge the image quality and give a quantitative score to measure the degree of image distortion; image quality evaluation is a key issue in the field of image processing, and image quality evaluation methods can be divided into subjective image quality evaluation methods and objective image quality evaluation methods . The subjective image quality evaluation method is based on the subjective feelings of the observers. Although it is accurate, it has the disadvantages of high cost and time-consuming; the objective image quality evaluation method uses a calculation model to automatically predict the image quality, which is low in cost and short in time. application prospects. Objective image quality assessment methods can ...

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Problems solved by technology

For semi-reference image quality evaluation, scholars at home and abroad have carried out extensive research. Wang used the natural image statistical model in the wavelet domain to extract the features of the reference image and the distorted image, and used the KL (Kullback-Leibler) distance between the features for semi-reference. Gao uses multi-order curvelets, bandlets, wavelets and contourlets transformation, and extracts the normalized histogram of the transformation coefficients for image quality evaluation; Engelke calculates the measurement of blocking effect, blurring and ringing effect respectively for image quality evaluation ; but these methods have shortcomings such as the evaluation accuracy to be improved and the extraction of too many semi-reference features that are not conducive to transmission.

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