Blind Image Quality Evaluation Method Based on Complementary Combination Features and Multiphase Regression

Abstract

The invention provides a blind image quality evaluation method based on complementary combination features and multiphase regression. In terms of feature extraction, complementary global frequency domain and local space frequency image features are used to more accurately capture image perception related information . In terms of prediction model construction, multiple support vector regression schemes are introduced, and its independent training sample set is constructed by searching the K nearest neighbors of each test image. Through the segment regression operation, the prediction accuracy of the perceptual quality prediction model can be effectively improved. Compared with the existing representative blind image quality evaluation method, the method of the present invention is more robust, and can obtain a prediction quality score that is more consistent with manual scoring.

Description

technical field [0001] The invention relates to image processing technology, in particular to perceptual visual signal processing technology. Background technique [0002] Image perceptual quality assessment method is the key technology to realize intelligent image quality assessment, network quality monitoring and image enhancement and other applications. At present, the mature full-reference and weak-reference image quality assessment methods require obtaining paired original image and distorted image information, and evaluating the quality by comparing the difference between them. However, in practical applications, the information of the original image is often not available. Therefore, an efficient blind image quality assessment method has become the breakthrough of this bottleneck. [0003] The blind image quality assessment method only needs the information of the distorted image itself to predict its perceptual quality, which can be applied to the judgment of camer...

AI Technical Summary

Problems solved by technology

Its main limitation is that it only uses the global statistical information of a single feature domain (such as airspace, DCT domain and wavelet domain) without considering the hierarchical properties of the human visual perception system; (2) In terms of perceptual quality regression, existing methods mainly Using single-phase support vector regression, all training samples are used to learn a unified support vector regression (SVR) model

Its obvious defect is that it does not consider the local characteristics of the test image, and cannot effectively fit the training data when dealing with complex feature space distributions.

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