Color fractal structure model for reduced-reference colorful image quality assessment

Authors:
Lihuo He;Dongxue Wang;Xuelong Li;Dacheng Tao;Xinbo Gao;Fei Gao
Affiliations:
School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, China;School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, China;Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, ...;Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, Australia;School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, China;School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, China
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Year:
2012

Citing 6
Cited 0

General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Mean for Subspace Selection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Image quality assessment based on multiscale geometric analysis

IEEE Transactions on Image Processing
Reduced-reference IQA in contourlet domain

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Quantifying color image distortions based on adaptive spatio-chromatic signal decompositions

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Developing reduced reference image quality assessment (RR-IQA) plays a vital role in dealing with the prediction of the visual quality of distorted images. However, most of existing methods fail to take color information into consideration, although the color distortion is significant for the increasing color images. To solve the aforementioned problem, this paper proposed a novel IQA method which focuses on the color distortion. In particular, we extract color features based on the model of color fractal structure. Then the color and structure features are mapped into visual quality using the support vector regression. Experimental results on the LIVE II database demonstrate that the proposed method has a good consistency with the human perception especially on images with color distortion.