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3D image quality measurement method based on deep learning

A technology of image quality and deep learning, applied in the field of image processing, can solve the problems of destroying the structural information of the original image and affecting the accuracy of stereoscopic image quality evaluation, so as to achieve the effect of improving reliability and accuracy and improving performance

Active Publication Date: 2018-01-26
TIANJIN UNIV
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Problems solved by technology

However, cutting the original large image into small image blocks will destroy the structural information of the original image, thus affecting the accuracy of stereoscopic image quality evaluation.

Method used

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  • 3D image quality measurement method based on deep learning
  • 3D image quality measurement method based on deep learning
  • 3D image quality measurement method based on deep learning

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Embodiment Construction

[0064] The present invention provides a new stereoscopic image quality evaluation method based on preprocessing the image into blocks, combined with the data set after principal component analysis and PCA dimension reduction, and then sending it into a multi-channel convolutional neural network. The algorithm proposed by the invention can more accurately and effectively evaluate the quality of stereoscopic images, and at the same time promote the development of stereoscopic imaging technology to a certain extent.

[0065] The invention proposes a stereoscopic image quality evaluation method based on convolutional neural network and principal component analysis. This method first performs region segmentation and principal component analysis (PCA) dimensionality reduction preprocessing on the image respectively, and then sends the obtained block data set and PCA dimensionality reduction data set into a multi-channel convolutional neural network; finally, the convolutional neural ...

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Abstract

The invention belongs to the image processing field and provides a novel three-dimensional image quality evaluation method, which can evaluate three-dimensional image quality more accurately and efficiently, and meanwhile, promote development of the stereoscopic imaging technique in a certain extent. According to the technical scheme, the 3D image quality measurement method based on deep learningis characterized by, to begin with, carrying out block segmentation processing on a stereo image data set to obtain a plurality of image blocks, and then, carrying out normalization processing on eachimage block; meanwhile, carrying out principal component analysis (PCA) dimension reduction processing on the stereo image data set to obtain lower-dimension images; then, sending an image block dataset obtained after block segmentation processing and a low-dimension data set obtained after PCA dimension reduction processing to a constructed convolutional neural network; extracting features layer by layer through the convolutional neural network; and finally, obtaining overall quality of the stereo images through a softmax classifier. The method is mainly used for image processing.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to the improvement and optimization of a stereoscopic image quality evaluation method, in particular to the application of deep learning convolutional neural network and principal component analysis in the objective evaluation of stereoscopic image quality. Background technique [0002] With the rapid development of mobile devices and communications, people are exposed to more and more picture content in their lives, especially the recently emerging 3D display technology and related applications have greatly improved the visual experience of the human eye, such as 3D movies, VR glasses, etc., have brought more entertainment and unique experience, which has attracted more researchers not only in the industry, but also in academia. How to effectively evaluate the quality of stereoscopic images in real time has become a hot topic in the field of stereoscopic image research. one of the key...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/46G06K9/62
Inventor 李素梅常永莉段志成侯春萍
Owner TIANJIN UNIV
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