Textural feature-based no-reference image quality evaluation method

A reference image and quality evaluation technology, which is applied in the field of image processing, can solve the problems of complex calculations and cannot meet real-time applications, and achieve the effects of low computational complexity, easy real-time implementation, and improved prediction accuracy

Inactive Publication Date: 2017-05-31
JIAXING UNIV
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Problems solved by technology

The above algorithm has the disadvantages of complex calculation and cannot meet real-time applications.

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  • Textural feature-based no-reference image quality evaluation method
  • Textural feature-based no-reference image quality evaluation method
  • Textural feature-based no-reference image quality evaluation method

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Embodiment Construction

[0039] The present invention will be further described below in conjunction with accompanying drawing.

[0040] Such as figure 1 with figure 2 As shown, a no-reference image quality evaluation method based on texture features, the specific steps are as follows:

[0041] Step 1. Randomly divide 29 original images and their distorted images in the LIVE image database of the University of Texas at Austin into two groups: 20 original images and their distorted images as the training image set, and 9 original images and their distorted images as the training image set. Test image set; the distorted images are divided into five distortion types: JPEG, JPEG2000, Blur, Noise and Fast Fading. Divide the input image into a training image set and a test image set, and perform feature extraction on each image in the training image set and the test image set;

[0042] Step 2. Carry out grayscale transformation to each image, convert the color image into a grayscale image, and then perf...

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Abstract

The invention relates to a textural feature-based no-reference image quality evaluation method. The method comprises the following specific steps of: dividing a graphic database into a training image set and a test image set; constructing images uder different scales for each image; solving normalized brightness images for the images under different scales; solving normalized gray-level co-occurrence matrixes in four directions for the normalized brightness images; calculating energy, entropies, contrast ratios and dependencies of the normalized gray-level co-occurrence matrixes to combine a feature vector; sending the feature vector and a subjective MOS score to a support vector machine to carry out training; and predicting test images by using the trained support vector machine so as to obtain an objective image quality evaluation result. According to the method, image quality evaluation is carried out by utilizing textural features; compared with the existing algorithms, the method is lower in calculation complexity and convenient to realize in real time, and has the effect of improving the prediction precision through extracting the textural features by utilizing the gray-level co-occurrence matrixes with a plurality of scales and a plurality of angles.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to an image quality evaluation method, in particular to a no-reference image quality evaluation method based on texture features. 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 met...

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Application Information

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IPC IPC(8): G06T7/00G06T7/45
Inventor 汪斌陈淑聪
Owner JIAXING UNIV
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