A Method of Image Quality Evaluation Based on Low Rank Sparse Matrix Factorization

Abstract

The invention discloses an image quality evaluation method based on low-rank sparse matrix decomposition. The method first randomly divides an input image into a training image set and a test image set, and converts the color distorted image in the training image set into a grayscale distorted image, and then Perform sparse and low-rank matrix decomposition to obtain an eigenvector. After combining the two eigenvectors, the combined subjective MOS score is sent to the support vector regression machine for training, and the trained support vector regression machine is obtained. Finally, the test image Also follow the above steps to extract feature vectors, send them to the trained support vector regression machine for testing, and obtain objective image quality evaluation results. The method of the present invention decomposes the input distorted image into a low-rank matrix and a sparse matrix to realize the effective separation of the foreground object and the background of the image, thereby extracting features from the foreground object and the background respectively, and using the extracted features to evaluate the image quality, which improves the efficiency of Accuracy of reference image quality assessment.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to an image quality evaluation method based on low-rank sparse matrix decomposition. 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, the image quality evaluation method can be divided into a full ...

AI Technical Summary

Problems solved by technology

Moorthy uses the statistical properties of wavelet coefficients for no-reference image quality evaluation, and Ye uses the visual codebook of Gabor filter coefficients to construct histograms for quality evaluation. Although these methods improve the accuracy of no-reference image quality evaluation, their results are not consistent with those of the human eye. There are still gaps in subjective image quality evaluation results

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