Reference-free image quality evaluation method based on image entropy

Abstract

The invention discloses a reference-free image quality evaluation method based on image entropy. The method comprises the following steps of (1) extracting image features to be tested, and identifyingthe probability pi of each distortion type of the image to be tested by using a support vector classifier (SVC), wherein the image features include mutual information between different color channels, two-dimensional entropy of gray image and two-dimensional entropy and mutual information of filtered sub-band image; 2 respectively assuming that the images to be tested belong to a certain distortion type, calculating a score qi corresponding to the distortion type by the support vector regression (SVR), and obtaining a quality index of the image to be tested by weight sum. Experiments on LIVEand TID2013 databases show that the algorithm has good performance and consistency of subjective and objective evaluation.

Description

technical field [0001] The invention relates to the technical field of image quality processing, in particular to an image entropy-based non-reference image quality evaluation method. Background technique [0002] We are in an era of information explosion, surrounded by overwhelming information every day. As a source of visual information, images contain a lot of valuable information. Because image information has incomparable advantages compared with other information, reasonable processing of image information has become an indispensable means in various fields. In the process of image processing and transmission, due to the imperfection of imaging system, processing method and transmission medium, plus object movement, noise pollution and other reasons, image distortion and degradation will inevitably be caused. The image quality directly affects people's subjective feelings and information acquisition. Therefore, the research on image quality assessment (IQA) has recei...

AI Technical Summary

Problems solved by technology

The existing explicit general-purpose NR-IQA algorithms face the following problems: 1) The color space of the image is seldom considered in the NR-IQA algorithm; 2) Some NR-IQA algorithms only use the statistical characteristics of pixels without considering the characteristics of the features. Spatial distribution

The algorithm based on one-dimensional entropy can reflect the aggregation characteristics of the gray distribution of the image, but it cannot reflect the spatial characteristics of the gray distribution of the image

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