Paper Info
Title
No reference image quality assessment metric via multi-domain structural information and piecewise regression
Abstract
We develop a new local image representation for capturing image quality.We design a novel piecewise regression for training the quality prediction function.The proposed algorithm outperforms many representative NR-IQA methods. The general purpose no reference image quality assessment (NR-IQA) is a challenging task, which faces two hurdles: (1) it is difficult to develop one quality aware feature which works well across different types of distortion and (2) it is hard to learn a regression model to approximate a complex distribution for all training samples in the feature space. In this paper, we propose a novel NR-IQA method that addresses these problems by introducing the multi-domain structural information and piecewise regression. The main motivation of our method is based on two points. Firstly, we develop a new local image representation which extracts the structural image information from both the spatial-frequency and spatial domains. This multi-domain description could better capture human vision property. By combining our local features with a complementary global feature, the discriminative power of each single feature could be further improved. Secondly, we develop an efficient piecewise regression method to capture the local distribution of the feature space. Instead of minimizing the fitting error for all training samples, we train the specific prediction model for each query image by adaptive online learning, which focuses on approximating the distribution of the current test image's k-nearest neighbor (KNN). Experimental results on three benchmark IQA databases (i.e., LIVE II, TID2008 and CSIQ) show that the proposed method outperforms many representative NR-IQA algorithms.
Year
DOI
Venue
2015
10.1016/j.jvcir.2015.08.009
Journal of Visual Communication and Image Representation
Keywords
Field
DocType
No reference image quality assessment,Human vision system,Image representation,Quality aware feature,Multi-domain structural information,Gradient of wavelet domain,Piecewise regression,HEVC
Computer vision,Feature vector,Feature detection (computer vision),Pattern recognition,Feature (computer vision),Regression analysis,Artificial intelligence,Distortion,Discriminative model,Standard test image,Mathematics,Segmented regression
Journal
Volume
Issue
ISSN
32
C
1047-3203
Citations 
PageRank 
References 
18
0.57
43
Authors
5
Name
Order
Citations
PageRank
Qingbo Wu139939.78
Hongliang Li21833101.92
Fanman Meng354933.61
King Ngi Ngan42383185.21
Shuyuan Zhu515624.72