No-reference image quality evaluation method based on deep forest classification

Abstract

The invention discloses a no-reference image quality evaluation method based on deep forest classification. The method comprises the following steps: step 1, image classification; step 2, extracting color quality characteristics of the image; step 3, extracting texture quality characteristics of the image; step 4, simulating the difference of different people on image quality cognition by utilizing the difference of decision tree extraction features in the deep forest classification model, and constructing the deep forest classification model to classify the image quality, including a multi-granularity scanning forest and a cascade forest; step 5, training the deep forest classification model based on the image quality features and the category labels thereof to obtain the probability thatthe test image belongs to different categories, i.e., statistical information of subjective evaluation results of different people on the image quality; step 6, setting a quality anchor, and fully considering the difference in the subjective evaluation process in combination with the probability that the image belongs to different categories to obtain a final image quality score. According to thenon-reference image quality evaluation method, the difference of different people for image quality cognition is simulated by using the deep forest, so that an image quality evaluation result is given. The method has 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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