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1.发明申请Objective assessment method for stereoscopic image quality combined with manifold characteristics and binocular characteristics 审中-公开立体图像质量的客观评估方法与歧管特征和双目特征相结合公开(公告)号：US20160350941A1
公开(公告)日：2016-12-01
申请号：US15233950
申请日：2016-08-11
Applicant: Ningbo University
Inventor： Mei Yu , Zhaoyun Wang , Fen Chen , Meiling He
IPC: G06T7/40
CPC classification number: G06T7/0002 , G06K9/4661 , G06K9/6252 , G06T2207/10012 , G06T2207/20081 , G06T2207/30168 , H04N13/00 , H04N13/133
Abstract: An objective assessment method for a stereoscopic image quality combined with manifold characteristics and binocular characteristics trains a matrix after dimensionality reduction and whitening obtained from natural scene plane images through an orthogonal locality preserving projection algorithm, for obtaining a best mapping matrix. Image blocks, not important for visual perception, are removed. After finishing selecting the image blocks, through the best mapping matrix, manifold characteristic vectors of the image blocks are extracted, and a structural distortion of a distorted image is measured according to a manifold characteristic similarity. Considering influences of an image luminance variation on human eyes, a luminance distortion of the distorted image is calculated according to a mean value of the image blocks. After obtaining the manifold similarity and the luminance similarity, quality values of the left and right viewpoint images are processed with linear weighting to obtain a quality value of the distorted stereoscopic image.
Abstract translation: 对于与歧管特征和双目特征组合的立体图像质量的客观评估方法，通过正交局部保留投影算法从自然场景平面图像获得的维数降低和白化之后训练矩阵，以获得最佳映射矩阵。 图像块，对视觉感知不重要，被删除。 在完成选择图像块之后，通过最佳映射矩阵，提取图像块的多重特征向量，并根据流形特征相似度来测量失真图像的结构失真。 考虑到图像亮度变化对人眼的影响，根据图像块的平均值计算失真图像的亮度失真。 在获得歧管相似性和亮度相似性之后，利用线性加权处理左和右视点图像的质量值，以获得失真的立体图像的质量值。
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公开(公告)号：US20170286798A1
公开(公告)日：2017-10-05
申请号：US15197604
申请日：2016-06-29
Applicant: Ningbo University
Inventor： Gangyi Jiang , Meiling He , Fen Chen , Yang Song
CPC classification number: G06K9/4652 , G06K9/00288 , G06K9/036 , G06K9/4642 , G06K9/4671 , G06K9/52 , G06K9/6201 , G06K9/6223 , G06K9/6247 , G06K2009/4695 , G06T7/0002 , G06T2207/20021 , G06T2207/20081 , G06T2207/30168
Abstract: An objective assessment method for a color image quality based on online manifold learning considers a relationship between a saliency and an image quality objective assessment. Through a visual saliency detection algorithm, saliency maps of a reference image and a distorted image are obtained for further obtaining a maximum fusion saliency map. Based on maximum saliencies of image blocks in the maximum fusion saliency map, a saliency difference between each reference image block and a corresponding distorted image block is measured through an absolute difference, and thus reference visual important image blocks and distorted visual important image blocks are screened and extracted. Through manifold eigenvectors of the reference visual important image blocks and the distorted visual important image blocks, an objective quality assessment value of the distorted image is calculated. The method has an increased assessment effect and a higher correlation between an objective assessment result and a subjective perception.
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公开(公告)号：US09892499B2
公开(公告)日：2018-02-13
申请号：US15233950
申请日：2016-08-11
Applicant: Ningbo University
Inventor： Mei Yu , Zhaoyun Wang , Fen Chen , Meiling He
CPC classification number: G06T7/0002 , G06K9/4661 , G06K9/6252 , G06T2207/10012 , G06T2207/20081 , G06T2207/30168 , H04N13/00 , H04N13/133
Abstract: An objective assessment method for a stereoscopic image quality combined with manifold characteristics and binocular characteristics trains a matrix after dimensionality reduction and whitening obtained from natural scene plane images through an orthogonal locality preserving projection algorithm, for obtaining a best mapping matrix. Image blocks, not important for visual perception, are removed. After finishing selecting the image blocks, through the best mapping matrix, manifold characteristic vectors of the image blocks are extracted, and a structural distortion of a distorted image is measured according to a manifold characteristic similarity. Considering influences of an image luminance variation on human eyes, a luminance distortion of the distorted image is calculated according to a mean value of the image blocks. After obtaining the manifold similarity and the luminance similarity, quality values of the left and right viewpoint images are processed with linear weighting to obtain a quality value of the distorted stereoscopic image.
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公开(公告)号：US09846818B2
公开(公告)日：2017-12-19
申请号：US15197604
申请日：2016-06-29
Applicant: Ningbo University
Inventor： Gangyi Jiang , Meiling He , Fen Chen , Yang Song
CPC classification number: G06K9/4652 , G06K9/00288 , G06K9/036 , G06K9/4642 , G06K9/4671 , G06K9/52 , G06K9/6201 , G06K9/6223 , G06K9/6247 , G06K2009/4695 , G06T7/0002 , G06T2207/20021 , G06T2207/20081 , G06T2207/30168
Abstract: An objective assessment method for a color image quality based on online manifold learning considers a relationship between a saliency and an image quality objective assessment. Through a visual saliency detection algorithm, saliency maps of a reference image and a distorted image are obtained for further obtaining a maximum fusion saliency map. Based on maximum saliencies of image blocks in the maximum fusion saliency map, a saliency difference between each reference image block and a corresponding distorted image block is measured through an absolute difference, and thus reference visual important image blocks and distorted visual important image blocks are screened and extracted. Through manifold eigenvectors of the reference visual important image blocks and the distorted visual important image blocks, an objective quality assessment value of the distorted image is calculated. The method has an increased assessment effect and a higher correlation between an objective assessment result and a subjective perception.
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