A no-reference image quality assessment method based on deep forest classification

Abstract

The invention discloses a no-reference image quality evaluation method based on deep forest classification, comprising: step 1, image classification; step 2, extracting the color quality feature of the image; step 3, extracting the texture quality feature of the image; step 4, using In the deep forest classification model, the decision tree extracts different features, simulates the difference in perception of image quality by different people, and constructs a deep forest classification model to classify image quality, including multi-granularity scanning forest and cascading forest; step 5, based on image quality Features and their category labels, train the deep forest classification model, and obtain the probability that the test image belongs to different categories, that is, the statistical information of the subjective evaluation results of the image quality by different people; step 6, set the quality anchor, and combine the images belonging to different categories The probability of taking into account the differences in the subjective evaluation process to obtain the final image quality score; the no-reference image quality evaluation method described in the present invention uses a deep forest to simulate the difference in image quality cognition of different people, thereby giving an image The quality evaluation results have important theoretical significance and practical value.

Description

technical field [0001] The invention relates to the fields of image processing technology, computer vision and artificial intelligence, in particular to a no-reference image quality evaluation method based on deep forest classification. Background technique [0002] With the popularization of image acquisition equipment, people began to use a large number of pictures to save and collect information in their daily life. However, the process of image acquisition, transmission, recovery and storage will cause different types and degrees of distortion, which seriously affects people's extraction and understanding of image information, and even easily generates wrong information, misleading people's understanding of images. Therefore, how to judge the quality of the image has become an urgent problem to be solved. [0003] According to different reference information, image quality evaluation methods can be divided into three categories: full reference, semi-reference and no ref...

AI Technical Summary

Problems solved by technology

This method achieves image quality evaluation through regional mutual information, but it takes more into account the local features of the image block, and ignores the overall quality features of the image.

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