Image quality evaluation method and device and computer readable storage medium

Abstract

The invention relates to the technical field of image quality evaluation, particularly provides an image quality evaluation method and device and a computer readable storage medium, and aims to solve the technical problem of how to improve the accuracy of image quality evaluation. For the purpose, the method provided by the embodiment of the invention comprises: obtaining the pixel feature of each pixel in a to-be-evaluated image, and obtaining a first image quality evaluation index value of the to-be-evaluated image according to the pixel feature; dividing the to-be-evaluated image into a plurality of image blocks, and performing image quality analysis on each image block by adopting a preset image quality evaluation model to obtain a second image quality evaluation index value of the to-be-evaluated image; obtaining a final image quality evaluation index value according to the first image quality evaluation index value and the second image quality evaluation index value; and performing image quality evaluation on the to-be-evaluated image according to the final image quality evaluation index value. Through the above steps, the accuracy of image quality evaluation can be improved.

Description

technical field [0001] The present invention relates to the technical field of image quality assessment, in particular to an image quality assessment method, device and computer-readable storage medium. Background technique [0002] Video structuring refers to data structuring processing of the video content in the video screen, extracting key information of the video content and performing semantic description. For example, when performing data structuring processing on pedestrians in video images, key information such as facial features, clothing, age, and height of pedestrians can be extracted and semantically described. In order to accurately extract key information about objects of interest such as pedestrians in video images, it is necessary to evaluate the image quality of each frame of video images to eliminate video images with poor image quality. [0003] When evaluating the sharpness of images, manual evaluation can be used to evaluate each frame of image one by ...

AI Technical Summary

Problems solved by technology

In practical applications, due to the inaccurate focus of the image acquisition device during the image acquisition process, the acquired image is blurred, but it is still easy to obtain key information of the object of interest, such as the clothing of pedestrians, using these blurred images. and height

However, the current conventional no-reference image quality assessment method will determine that the image is a low-quality image after analyzing the image to a certain degree of blur. As a result, the number of video frames that can be effectively used in video structured processing is reduced, which is not conducive to the extraction of key information about objects of interest such as pedestrians

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