CLC number: TP753
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2016-09-26
Cited: 0
Clicked: 7459
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.
@article{title="Image quality assessment method based on nonlinear feature extraction in kernel space",
author="Yong Ding, Nan Li, Yang Zhao, Kai Huang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="17",
number="10",
pages="1008-1017",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500439"
}
%0 Journal Article
%T Image quality assessment method based on nonlinear feature extraction in kernel space
%A Yong Ding
%A Nan Li
%A Yang Zhao
%A Kai Huang
%J Frontiers of Information Technology & Electronic Engineering
%V 17
%N 10
%P 1008-1017
%@ 2095-9184
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500439
TY - JOUR
T1 - Image quality assessment method based on nonlinear feature extraction in kernel space
A1 - Yong Ding
A1 - Nan Li
A1 - Yang Zhao
A1 - Kai Huang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
IS - 10
SP - 1008
EP - 1017
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1500439
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.
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