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Full-reference stereoscopic image quality evaluation method based on saliency

A stereoscopic image and quality evaluation technology, applied in the field of image processing, to achieve the effect of strong consistency and excellent performance

Active Publication Date: 2017-12-15
TIANJIN UNIV
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AI Technical Summary

Problems solved by technology

[0006] Aiming at the problem of evaluating the quality of stereoscopically distorted images, the present invention proposes a full-reference stereoscopic image quality evaluation method that can obtain better stereoscopic image quality evaluation effects

Method used

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  • Full-reference stereoscopic image quality evaluation method based on saliency
  • Full-reference stereoscopic image quality evaluation method based on saliency
  • Full-reference stereoscopic image quality evaluation method based on saliency

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Embodiment Construction

[0022] The present invention proposes a full reference image quality evaluation method based on extracting the energy of the saliency map on the left image, the right image and the composite image as the weight of the gain control. In order to make the technical solutions of the present invention clearer, the specific embodiments of the present invention are further described below.

[0023] 1. Synthetic graph generation

[0024] Stereo images are different from plane images mainly because they have binocular features. According to the binocular competition characteristics, the left and right images are fused to obtain a composite image, such as figure 1 , the present invention defines the composite graph as:

[0025] C(x,y)=w L (x,y)·I L (x,y)+w R (x+d,y)·I R (x+d,y)

[0026] (1)

[0027] Among them, C represents the composite image, I L and I R represent the left image and the right image, respectively, d is the disparity, w L and w R is the left and right weight...

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Abstract

The invention relates to a full-reference stereoscopic image quality evaluation method based on saliency. The method comprises the following steps: for an original stereoscopic image and a distorted stereoscopic image, separately generating a composite graph according to the weight of the own left image and right image; separately performing visual saliency graph extraction for the left image, the right image and the composite graph of the original stereoscopic image; separately calculating color saliency components, frequency saliency components and location saliency components for the left image, the right image and the composite graph of the original stereoscopic image to obtain a final visual saliency graph; and taking the visual saliency graph energy as the weight, and merging the visual saliency graph of the left image, the right image and the composite graph of the original stereoscopic image with the structural similarity of the left image, the right image and the composite graph to obtain a final quality evaluation value by adopting a gain control method.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to the objective evaluation of stereoscopic image quality, and relates to an objective image quality evaluation method using a saliency map and a stereoscopic image composite map. Background technique [0002] With the development of science and technology, the video technology is developing towards three-dimensional, networked and digital multi-faceted development. Due to hardware and environmental factors, the video system will inevitably introduce unpredictable distortion when collecting information. Based on this situation, it is necessary to relatively accurately predict the introduced distortion. [0003] Compared with the traditional flat image quality evaluation, the stereo image quality evaluation is more challenging. Because viewing stereoscopic images for a long time will bring some adverse symptoms, such as dizziness, nausea and vomiting, which will directly affect the h...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N17/00
CPCH04N17/004
Inventor 侯春萍林洪湖岳广辉
Owner TIANJIN UNIV
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