TY - GEN
T1 - Reduced reference image quality assessment via sub-image similarity based redundancy measurement
AU - Mou, Xuanqin
AU - Xue, Wufeng
AU - Zhang, Lei
PY - 2012/4/11
Y1 - 2012/4/11
N2 - The reduced reference (RR) image quality assessment (IQA) has been attracting much attention from researchers for its loyalty to human perception and flexibility in practice. A promising RR metric should be able to predict the perceptual quality of an image accurately while using as few features as possible. In this paper, a novel RR metric is presented, whose novelty lies in two aspects. Firstly, it measures the image redundancy by calculating the so-called Sub-image Similarity (SIS), and the image quality is measured by comparing the SIS between the reference image and the test image. Secondly, the SIS is computed by the ratios of NSE (Non-shift Edge) between pairs of sub-images. Experiments on two IQA databases (i.e. LIVE and CSIQ databases) show that by using only 6 features, the proposed metric can work very well with high correlations between the subjective and objective scores. In particular, it works consistently well across all the distortion types.
AB - The reduced reference (RR) image quality assessment (IQA) has been attracting much attention from researchers for its loyalty to human perception and flexibility in practice. A promising RR metric should be able to predict the perceptual quality of an image accurately while using as few features as possible. In this paper, a novel RR metric is presented, whose novelty lies in two aspects. Firstly, it measures the image redundancy by calculating the so-called Sub-image Similarity (SIS), and the image quality is measured by comparing the SIS between the reference image and the test image. Secondly, the SIS is computed by the ratios of NSE (Non-shift Edge) between pairs of sub-images. Experiments on two IQA databases (i.e. LIVE and CSIQ databases) show that by using only 6 features, the proposed metric can work very well with high correlations between the subjective and objective scores. In particular, it works consistently well across all the distortion types.
KW - image quality assessment (IQA)
KW - NSE
KW - reduced reference
KW - redundancy measurement
KW - sub-image similarity
UR - http://www.scopus.com/inward/record.url?scp=84859471535&partnerID=8YFLogxK
U2 - 10.1117/12.908161
DO - 10.1117/12.908161
M3 - Conference article published in proceeding or book
AN - SCOPUS:84859471535
SN - 9780819489425
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Proceedings of SPIE-IS and T Electronic Imaging - Human Vision and Electronic Imaging XVII
T2 - Human Vision and Electronic Imaging XVII
Y2 - 23 January 2012 through 26 January 2012
ER -