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A reference-free contrast distortion image quality evaluation method based on contrast enhancement

A technology for distorted images and quality evaluation, applied in the field of image processing

Active Publication Date: 2019-06-14
JIAXING UNIV
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Problems solved by technology

Existing full-reference image quality assessment methods are mainly aimed at ordinary images, while relatively few studies have been conducted on contrast-distorted images, so it is more challenging to perform full-reference image quality assessment on contrast-distorted images

Method used

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  • A reference-free contrast distortion image quality evaluation method based on contrast enhancement
  • A reference-free contrast distortion image quality evaluation method based on contrast enhancement
  • A reference-free contrast distortion image quality evaluation method based on contrast enhancement

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Embodiment Construction

[0069] Below in conjunction with accompanying drawing and implementation example the present invention is described in detail:

[0070] In the specific implementation, the CID2013 database is used as the experimental database. The CID2013 database is a database dedicated to the evaluation of contrast-distorted images, including 480 contrast-distorted images;

[0071] Step (1): Take out the contrast distorted image I from the input image set, and convert the color distorted image in the training image set into a grayscale distorted image;

[0072] Step (2): Carry out histogram equalization to the contrast gray scale distorted image I, concrete steps are:

[0073] (2.1): the number of pixels n of statistical contrast grayscale distortion image I grayscale i i , where i=0,1,...,L, L is the total number of gray levels, and the value of L is 255;

[0074] (2.2): Calculate the histogram of the contrast grayscale distortion image I, wherein the calculation formula is as follows:

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Abstract

The invention discloses a reference-free contrast distortion image quality evaluation method based on contrast enhancement. The method comprises the following steps of firstly, calculating a structural similarity index mean value and a variance between a distortion image and a contrast enhancement image as contrast enhancement characteristics; dividing the contrast distortion image into a plurality of superpixels, and calculating the mean value and the variance of the superpixels as superpixel characteristics; calculating the deviation of the contrast distortion image in the 0-degree direction, the 45-degree direction, the 90-degree direction and the 135-degree direction at each pixel point to form a deviation matrix, obtaining the deviation matrix characteristic values, and then obtainingthe deviation characteristics through calculation; combining the features to obtain a final feature vector; and sending into a support vector machine for training and testing to obtain an objective image quality evaluation result. The method fully considers the histogram of the image, the superpixel segmentation, the gray scale local deviation and the contrast distortion, and improves the qualityevaluation precision of the contrast distortion image.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a method for evaluating image quality without reference contrast distortion based on contrast enhancement. 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...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T5/00G06T5/40G06T7/10G06T7/62
Inventor 汪斌陈淑聪
Owner JIAXING UNIV
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