Blind image quality evaluation method based on complementarity combination characteristics and multiphase regression

Abstract

The invention provides a blind image quality evaluation method based on complementarity combination characteristics and multiphase regression. On the aspect of characteristic extraction, image perception related information can be more accurately captured through overall frequency domain image characteristics and local empty domain image characteristics, wherein the overall frequency domain image characteristics and the local empty domain image characteristics have complementarity. On the aspect of prediction model construction, multiple supporting vector regression schemes are introduced, and the independent training sample set of each test image is established by searching for K pairs of neighbors of the test image. Through the segmented regression operation, the prediction accuracy of a perception quality prediction model can be effectively improved. Compared with an existing representative blind image quality evaluation method, the method has higher robustness, and the prediction quality grade more consistent with the grade obtained through manual work can be obtained.

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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