Paper Info
Title
Reorganized DCT-based image representation for reduced reference stereoscopic image quality assessment.
Abstract
In this paper, a novel reduced reference (RR) stereoscopic image quality assessment (SIQA) is proposed by characterizing the statistical properties of the stereoscopic image in the reorganized discrete cosine transform (RDCT) domain. Firstly, the difference image between the left and right view images is computed. Afterwards, the left and right view images, as well as the difference image, are decomposed by block-based discrete cosine transform (DCT). The DCT coefficients are further reorganized into a three-level coefficient tree, resulting in ten RDCT subbands. For each RDCT subband, the statistical property of the coefficient distribution is modeled by the generalized Gaussian density (GGD) function. And the mutual information (MI) and energy distribution ratio (EDR) are employed to depict the statistical properties across different RDCT subbands. Moreover, EDR can further model the mutual masking property of the human visual system (HVS). By considering the GGD modeling behavior within each RDCT subband and MI together EDR characterizing behavior across RDCT subbands, the statistical properties of the stereoscopic image are fully exploited, including the left view, right view, and the difference image. Experimental results demonstrated that the statistical properties of the difference image can well represent the perceptual quality of the stereoscopic image, which outperforms the representative RR quality metrics for stereoscopic image and even some full reference (FR) quality metrics. By considering the left view, right view, and difference image together, the performances of the proposed RR SIQA can be further improved, which presenting a more closely relationship between the quality metric output and human visual perception. (C) 2016 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2016
10.1016/j.neucom.2015.06.116
Neurocomputing
Keywords
Field
DocType
NO-REFERENCE IMAGE,VIDEO QUALITY,STATISTICS,DOMAIN,SEGMENTATION,COMPRESSION,JPEG2000,MODEL
Computer vision,Masking (art),Pattern recognition,Stereoscopy,Human visual system model,Discrete cosine transform,Image representation,Image quality,Gaussian,Artificial intelligence,Mutual information,Mathematics
Journal
Volume
Issue
ISSN
215
C
0925-2312
Citations 
PageRank 
References 
14
0.54
42
Authors
4
Name
Order
Citations
PageRank
Lin Ma191271.35
Xu Wang2936.83
Qiong Liu3877.70
King Ngi Ngan42383185.21