No-reference image quality evaluation method based on structure similarity mapping dictionary learning

Abstract

The present invention discloses a no-reference image quality evaluation method based on structure similarity mapping dictionary learning. The method comprises the steps of: performing joint training of a structure similarity mapping image in a dictionary training image set and a reference image to obtain a joint dictionary, employing the joint dictionary to perform sparse decomposition of distorted images in an evaluation training image set and an evaluation test image set to obtain a structure similarity mapping graph, extracting gray co-occurrence matrixes in various directions from the structure similarity mapping graph, and converting the gray co-occurrence matrixes to vectors, calculating the standard deviation, the skewness and the kurtosis of the vectors of the gray co-occurrence matrixes in multiple scales to combine feature vectors, sending the feature vectors to a support vector regression for training and test, and performing prediction to obtain an objective quality evaluation score. The method performs training to obtain the structure similarity mapping dictionary, employs the structure similarity mapping dictionary for feature extraction and is identical to nerve characteristics of a brain visual cortex so as to obtain a no-reference image quality evaluation result with a more accurate prediction effect.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a no-reference image quality evaluation method based on structural similarity dictionary learning. Background technique [0002] Image quality evaluation is a key issue in the field of image processing. Image quality evaluation methods can be divided into subjective image quality evaluation methods and objective image quality evaluation methods according to whether people participate. Subjective image quality evaluation methods are scored by humans, and the evaluation results are accurate, but the evaluation process is complex, time-consuming, and difficult to be applied in real time. The objective image quality evaluation method does not require human participation, and the image quality is automatically predicted by a specific computer algorithm. According to whether the original undistorted image is used as a reference, objective image quality assessment methods can be divided...

AI Technical Summary

Problems solved by technology

Since the method for a specific type of distortion needs to know its type of distortion, its scope of application is limited, so research on general methods applicable to multiple types of distortion has become a hot spot in the field of image qual

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