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CLC number: TP753

On-line Access: 2016-10-08

Received: 2015-12-07

Revision Accepted: 2016-07-04

Crosschecked: 2016-09-26

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yong Ding

http://orcid.org/0000-0002-5226-7511

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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.10 P.1008-1017

10.1631/FITEE.1500439


Image quality assessment method based on nonlinear feature extraction in kernel space


Author(s):  Yong Ding, Nan Li, Yang Zhao, Kai Huang

Affiliation(s):  Institute of VLSI Design, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   dingy@vlsi.zju.edu.cn, linan@vlsi.zju.edu.cn

Key Words:  Image quality assessment, Full-reference method, Feature extraction, Kernel space, Support vector regression


Yong Ding, Nan Li, Yang Zhao, Kai Huang. Image quality assessment method based on nonlinear feature extraction in kernel space[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(10): 1008-1017.

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journal="Frontiers of Information Technology & Electronic Engineering",
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pages="1008-1017",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500439"
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Abstract: 
To match human perception, extracting perceptual features effectively plays an important role in image quality assessment. In contrast to most existing methods that use linear transformations or models to represent images, we employ a complex mathematical expression of high dimensionality to reveal the statistical characteristics of the images. Furthermore, by introducing kernel methods to transform the linear problem into a nonlinear one, a full-reference image quality assessment method is proposed based on high-dimensional nonlinear feature extraction. Experiments on the LIVE, TID2008, and CSIQ databases demonstrate that nonlinear features offer competitive performance for image inherent quality representation and the proposed method achieves a promising performance that is consistent with human subjective evaluation.

基于核空间非线性特征提取的图像质量评价方法

概要:在实现对与人类视觉感知相一致的图像质量的客观评价中,如何提取图像的视觉感知特征至关重要。不同于传统方法中通过线性变换或模型表达图像的方式,本文采用高维空间的一种数学表达来揭示图像的统计特性,通过引入核独立分量分析(kernel independent component analysis, KICA)方法实现非线性转化和图像的高维特征提取。从而提出一种基于非线性特征提取的全参考图像质量评价方法。在LIVE、TID2008和CSIQ等图像质量评价数据库上的实验结果表明,图像的非线性特征更有利于图像内在质量的描述,并且本文提出的方法性能良好,与主观评价较为一致。

关键词:图像质量评价;全参考方法;特征提取;核空间;支持向量回归

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