Method for evaluating visual comfort degree of three-dimensional image

A technology of stereoscopic images and evaluation methods, applied in stereoscopic systems, image communication, television, etc., can solve problems such as inability to predict accurately

Active Publication Date: 2014-02-12
贤珹(上海)信息科技有限公司
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Problems solved by technology

However, according to the attention characteristics of human stereo vision, the human eye is only sensitive to the visual comfort / discomfort of some visually important areas. Accurate prediction to obtain objective evaluation value

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  • Method for evaluating visual comfort degree of three-dimensional image
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  • Method for evaluating visual comfort degree of three-dimensional image

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[0076] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0077] A method for evaluating visual comfort of stereoscopic images proposed by the present invention, its overall realization block diagram is as follows figure 1 As shown, it includes the following steps:

[0078] ① Denote the left viewpoint image of the stereoscopic image to be evaluated as {I L (x, y)}, denote the right viewpoint image of the stereo image to be evaluated as {I R (x,y)}, denote the right disparity image of the stereo image to be evaluated as {d R (x,y)}, where (x,y) means {I L (x,y)}, {I R (x,y)} and {d R The coordinate position of the pixel point in (x, y)}, 1≤x≤W, 1≤y≤H, W means {I L (x,y)}, {I R (x,y)} and {d R The width of (x,y)}, H means {I L (x,y)}, {I R (x,y)} and {d R (x,y)} height, I L (x,y) means {I L The pixel value of the pixel whose coordinate position is (x, y) in (x, y)}, I R (x,y) means {I R ...

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Abstract

The invention discloses a method for evaluating the visual comfort degree of a three-dimensional image. The method comprises the steps that first a three-dimensional saliency map of a right viewpoint image is obtained by extracting a saliency map of the right viewpoint image and a deep saliency map of a right parallax image, then parallax amplitude, parallax gradient and spatial frequency characteristics are extracted according to three-dimensional saliency map weighing, a characteristic vector of the three-dimensional image is obtained, characteristic vectors of all the three-dimensional images in a three-dimensional image set are trained by utilizing support vector regression, finally each three-dimensional image in the three-dimensional image set is tested by utilizing a support vector regression training model obtained through training, and an objective visual comfort degree evaluation predicted value is obtained. The method has the advantages that characteristic vector information of the three-dimensional images has strong stability and can well reflect the variation situation of the visual comfort degree of the three-dimensional images, so that correlation of an objective evaluation result and subjective perception is effectively improved.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to a stereoscopic image visual comfort evaluation method. Background technique [0002] With the rapid development of stereoscopic video display technology and high-quality stereoscopic video content acquisition technology, the quality of experience (QoE, quality of experience) of stereoscopic video is an important issue in the design of stereoscopic video systems, and visual comfort (VC, visual comfort) is an important factor affecting the visual experience quality of stereoscopic video. At present, research on the quality evaluation of stereoscopic video / stereoscopic images mainly considers the influence of content distortion on image quality, but seldom considers the influence of factors such as visual comfort. Therefore, in order to improve the visual experience quality of viewers, it is very important to study the objective evaluation model of visual comfort of stereoscopi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N17/00H04N13/00
Inventor 邵枫姜求平蒋刚毅郁梅李福翠彭宗举
Owner 贤珹(上海)信息科技有限公司
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