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No-reference Stereo Image Quality Evaluation Method Based on Generalized Regression Neural Network

A technology of stereoscopic image and generalized regression, applied in image communication, stereoscopic system, television, etc., to achieve the effect of improving correlation

Active Publication Date: 2017-11-28
嘉兴智旭信息科技有限公司
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Problems solved by technology

Therefore, it is not possible to simply extend the existing single-view visual quality no-reference evaluation model directly to the no-reference stereo image quality evaluation method

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  • No-reference Stereo Image Quality Evaluation Method Based on Generalized Regression Neural Network
  • No-reference Stereo Image Quality Evaluation Method Based on Generalized Regression Neural Network

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Embodiment Construction

[0019] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0020] A kind of non-reference stereoscopic image quality evaluation method based on generalized regression neural network proposed by the present invention, its overall realization block diagram is as follows figure 1 As shown, it includes the following steps:

[0021] ① Order S dis Indicates the distorted stereo image to be evaluated, and S dis The left view image of is denoted as {L dis (x,y)}, the S dis The right view image of is denoted as {R dis(x,y)}, where, 1≤x≤W, 1≤y≤H, W means S dis The width, H means S dis height, L dis (x,y) means {L dis The pixel value of the pixel whose coordinate position is (x, y) in (x, y)}, R dis (x,y) means {R dis The pixel value of the pixel whose coordinate position is (x, y) in (x, y)}.

[0022] ②Respectively for {L dis (x,y)} and {R dis (x,y)} implement log-Gabor filtering to get {L dis (x,...

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Abstract

The invention discloses a generalized regression neural network based non-reference stereoscopic image quality evaluation method. The method includes obtaining a binocular energy image of a left view point and a right view point according to amplitude images and phase images of a left view point image and a right view point image of a to-be-evaluated distortion stereoscopic image and a parallax image between the left view point image and the right view point image, and further obtaining a normalized histogram statistic feature vector subjected to binocular energy modulation; obtaining the feature vector in the same way as for each distortion stereoscopic image in a training set, training subjective scoring and feature vectors of all the distortion stereoscopic images in the training set, obtaining a generalized regression neural network training model, and finally testing the normalized histogram statistic feature vector subjected to binocular energy modulation of the to-be-evaluated distortion stereoscopic image and obtaining a subjective quality evaluation predication value. The method has an advantage of fully considering the stereoscopic vision perception characteristics, so that the relevance between an objective evaluation result and subjective perception can be improved effectively.

Description

technical field [0001] The invention relates to a method for objectively evaluating the quality of a stereoscopic image, in particular to a method for evaluating the quality of a stereoscopic image without reference based on a generalized regression neural network. Background technique [0002] Since entering the 21st century, with the maturity of stereoscopic image / video system processing technology and the rapid development of computer network and communication technology, people have a strong demand for stereoscopic image / video system. Compared with the traditional single-viewpoint image / video system, the stereoscopic image / video system is more and more popular because it can provide depth information to enhance the visual reality and give users an immersive new visual experience. It is considered to be the main development direction of the next-generation media, and has aroused widespread concern in the academic and industrial circles. However, in order to obtain better...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04N17/00H04N13/00
CPCH04N13/106H04N17/004H04N2013/0074
Inventor 周武杰张爽爽潘婷蔡星宇顾鹏笠郑飘飘岑岗王中鹏周扬吴茗蔚邱薇薇陈芳妮郑卫红陈寿法陶坚葛丁飞
Owner 嘉兴智旭信息科技有限公司
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