Image just noticeable distortion threshold estimation method based on texture masking effect

A texture masking, image technology, applied in image enhancement, image analysis, image data processing and other directions, can solve problems such as inaccurate estimation

Active Publication Date: 2021-07-30
NINGBO UNIV
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Problems solved by technology

According to the characteristics of the Human Visual System (HVS), compared with areas with relatively flat textures, HVS has a higher tolerance for distortion in areas with rough textures. The ...

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  • Image just noticeable distortion threshold estimation method based on texture masking effect
  • Image just noticeable distortion threshold estimation method based on texture masking effect
  • Image just noticeable distortion threshold estimation method based on texture masking effect

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Embodiment Construction

[0027] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0028] According to the characteristics of the human visual system, for areas with rough texture in the image, the human eye will instinctively reduce the acquisition of information, and for areas with smoother texture in the image, the human eye tends to obtain more information. Therefore, for the image In areas with relatively rough textures, the human eye can tolerate a greater degree of distortion, and for areas with smoother textures in the image, the degree of distortion that the human eye can tolerate is lower. None of the previous methods can accurately describe the texture roughness of the local area of ​​the image. For this reason, the present invention proposes a method for estimating the threshold value of the image's perceptible distortion based on the texture masking effect. The overall realization block diagram is as follows: fi...

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Abstract

The invention discloses an image just noticeable distortion threshold estimation method based on a texture masking effect, which comprises the following steps of converting a grayscale image of an original image into a double-precision floating point type image, calculating fractal dimension of each image block in the image by adopting a differential box counting method, and taking the fractal dimension as a weight for measuring the texture roughness of the image block, obtaining a weight graph; calculating the texture complexity of each pixel point in the grayscale image according to the weight image, obtaining a texture masking effect according to the texture complexity and the brightness contrast, and obtaining a contrast masking effect according to the brightness contrast; extracting an object edge; according to the texture masking effect, the contrast masking effect and the binary image corresponding to the object edge, obtaining a space masking effect of each pixel point in the gray level image; obtaining an initial just noticeable distortion threshold according to the spatial masking effect and the brightness adaptability, and obtaining a final just noticeable distortion threshold in combination with the saliency map of the grayscale image; the method has the advantage of high just-noticeable distortion threshold estimation accuracy.

Description

technical field [0001] The invention relates to an image just noticeable distortion (Just Noticeable Difference, JND) threshold estimation method, in particular to a texture masking effect-based image just noticeable distortion threshold estimation method. Background technique [0002] With the emergence of different social software, people are more willing to upload their own life and entertainment photos or videos on these social software or networks to share their pleasures with others. This involves the image encoding task, how to hide useless and retain effective information is the key to saving network resources, and the JND threshold estimation technology is just for this purpose. In the past ten years, many JND threshold estimation methods have been proposed. However, most of the methods cannot estimate the JND threshold of the image very accurately. For example, some methods only consider the influence of brightness on the JND threshold of the image. , although som...

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Application Information

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IPC IPC(8): G06T7/11G06T5/00G06T5/30
CPCG06T7/11G06T5/007G06T5/30G06T2207/20024G06T2207/20192
Inventor 姜求平郭嘉骏邵枫
Owner NINGBO UNIV
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