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Non-reference stereo image quality evaluation method based on local-to-global feature regression

A technology of stereoscopic images and global features, applied in the field of image processing, to achieve the effect of excellent performance

Pending Publication Date: 2019-12-13
TIANJIN UNIV
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AI Technical Summary

Problems solved by technology

Although the medical imaging mechanism of stereo vision is still unclear, literature [9] shows that the fusion of two viewpoints occurs in each region

Method used

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  • Non-reference stereo image quality evaluation method based on local-to-global feature regression
  • Non-reference stereo image quality evaluation method based on local-to-global feature regression
  • Non-reference stereo image quality evaluation method based on local-to-global feature regression

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Experimental program
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Embodiment Construction

[0026] The network model of the present invention includes three channels (ie, left channel, right channel and fusion channel), and adopts two-step regression for training. In Step 1, the FSIM algorithm is first used to calculate the scores of the corresponding reference image and distorted image patches as labels to guide CNN to perform local regression training. After Step 1 is over, save the parameters to optimize the training of Step 2. In Step 2, the feature maps obtained from the left and right channels are concatenated with the fusion channel, and then global regression is performed based on the model of Step 1 by using DMOS as the label.

[0027] The experiment of the present invention is carried out on the publicly available LIVE 3D image database. The LIVE 3D image database includes two separate databases, phase-I and phase-II. The stereoscopic images are presented as plane images from the left and right viewpoints, and the size is 360×640. Among them, phase-I cont...

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Abstract

The invention belongs to the field of image processing and aims to establish an efficient no-reference stereo image quality evaluation method. The quality prediction is more accurate and the efficiency of three-dimensional image quality evaluation work is improved. According to the non-reference stereo image quality evaluation method based on local to global feature regression, firstly, differentlabels are given to image blocks of a left viewpoint and a right viewpoint through a feature similarity FSIM algorithm, the calculated labels are used for guiding networks of a left channel and a right channel to conduct pre-training at the same time, and therefore local regression of features is achieved; then, a fusion channel is added on the basis of the left channel and the right channel to form a global regression network, on the basis of the pre-training model, a subjective evaluation value DMOS serves as a label to guide network training, network parameters are finely adjusted, and therefore global regression of features is achieved; and the quality of the three-dimensional image is subjected to feature extraction and prediction by the trained global regression network. The method is mainly applied to design and manufacturing occasions.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to the application of deep learning in stereoscopic image quality evaluation, and in particular to a no-reference stereoscopic image quality evaluation method based on binocular fusion from local to global feature regression. Background technique [0002] In recent years, with the development and integration of computer graphics, computer vision, multimedia and other related technologies, stereoscopic visualization has attracted more and more people's interest. Today, with the explosion of digital information, three-dimensional technology continues to mature, and three-dimensional products continue to enrich people's lives. More and more 3D movies occupy the screen, so that people can feel the picture more vividly, and more and more video phones, 3D games, and mobile TVs use stereoscopic technology to obtain better visual experience. In addition, stereoscopic image technology is also ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62
CPCG06T7/0002G06T2207/10012G06T2207/20081G06T2207/30168G06T2207/20221G06F18/22
Inventor 李素梅薛建伟王明毅
Owner TIANJIN UNIV
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