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Color image quality estimating method based on super-complex

A color image and quality assessment technology, applied in the direction of color signal processing circuits, etc., can solve the problems of not considering the internal relationship of the RGB three-color information of color images, and unable to handle color image evaluation.

Inactive Publication Date: 2007-02-14
FUDAN UNIV
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AI Technical Summary

Problems solved by technology

[0005] Since the above methods are all discussed based on grayscale images, when discussing the quality of color images, people extract the grayscale information of color images through some transformation, and then use the quality indicators of grayscale images to process them. Realize that the components are processed by the grayscale method and then add the three results obtained, but this method is still unable to deal with the evaluation of color images with color changes, and they do not consider the internal relationship between the RGB three-color information of color images

Method used

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  • Color image quality estimating method based on super-complex
  • Color image quality estimating method based on super-complex
  • Color image quality estimating method based on super-complex

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Embodiment Construction

[0035] Here we take the Lena image as an example. First, different types of distortion processing are performed on the Lena image, the image compressed by the JPEG method, the image with Gaussian noise, the image with salt and pepper noise, and DC-shifting (meaning that the R, G, and B components are added. A certain value above) is obtained through MATLAB processing; and the blurred image, the sharpened image, and the image with improved contrast are processed by the CxImage class library; the color rotation is the method in the literature [10]. Rotate the color vector by a certain angle around the axis [0.58-0.58-0.57]. Distortion types and processing are shown in Table 1:

[0036] Vague

contrast

DC-shifting

Gaussian noise

salt and pepper noise

jpeg compression

color rotation

1

1

(50，20，

-20)

6

0.002

10∶1

20°

[0037] The parameters of JPEG in Table 1 represent image...

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Abstract

The invention is a color image quality evaluation method based on super-complex relating to image quality evaluation technical field. It describes three-color R, G, S values of color image as a whole super-complex vector, and indicates the common quality indexes of color image which are based on five combinations of color image false, correlating expense, luminance distortion, contrast and color distortion. According to HSI (hue, saturation, brightness) color model, the relationship between the mentioned common quality indexes of color image and gray image is discussed. The experimental results from various color image distortions show that the mentioned common quality indexes are not only prior to the existing method on the assessment of gray part, but also able to determine the distortion mainly occurs in the structure or color information.

Description

technical field [0001] The invention belongs to the technical field of image quality evaluation, in particular to a color image quality evaluation method based on hypercomplex numbers. Background technique [0002] If you want to describe the internal connection of the color image R (red), G (green), and B (blue) three-color components, hypercomplex numbers can take the three-color components of the color image as a vector as a whole and perform the following pure four without real part Metadata description [8]: [0003] In the process of image acquisition, compression, storage, transmission and reproduction, digital images often produce a large number of different types and levels of distortion, which will lead to a serious decline in image quality. In some applications, images are monitored and controlled by humans, so the evaluation of image quality is ultimately determined by human subjective evaluation. In other applications, subjective evaluation is often inconvenient...

Claims

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Application Information

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IPC IPC(8): H04N9/64
Inventor 郝明非张建秋
Owner FUDAN UNIV
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