Blind image quality assessment : from natural scene statistics to perceptual quality

© 2011 IEEE

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 20(2011), 12 vom: 30. Dez., Seite 3350-64
1. Verfasser: Moorthy, Anush Krishna (VerfasserIn)
Weitere Verfasser: Bovik, Alan Conrad
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2011
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.
LEADER 01000naa a22002652 4500
001 NLM207747199
003 DE-627
005 20231224002736.0
007 cr uuu---uuuuu
008 231224s2011 xx |||||o 00| ||eng c
024 7 |a 10.1109/TIP.2011.2147325  |2 doi 
028 5 2 |a pubmed24n0692.xml 
035 |a (DE-627)NLM207747199 
035 |a (NLM)21521667 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Moorthy, Anush Krishna  |e verfasserin  |4 aut 
245 1 0 |a Blind image quality assessment  |b from natural scene statistics to perceptual quality 
264 1 |c 2011 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Completed 19.03.2012 
500 |a Date Revised 22.11.2011 
500 |a published: Print-Electronic 
500 |a Citation Status PubMed-not-MEDLINE 
520 |a © 2011 IEEE 
520 |a Our approach to blind image quality assessment (IQA) is based on the hypothesis that natural scenes possess certain statistical properties which are altered in the presence of distortion, rendering them un-natural; and that by characterizing this un-naturalness using scene statistics, one can identify the distortion afflicting the image and perform no-reference (NR) IQA. Based on this theory, we propose an (NR)/blind algorithm-the Distortion Identification-based Image Verity and INtegrity Evaluation (DIIVINE) index-that assesses the quality of a distorted image without need for a reference image. DIIVINE is based on a 2-stage framework involving distortion identification followed by distortion-specific quality assessment. DIIVINE is capable of assessing the quality of a distorted image across multiple distortion categories, as against most NR IQA algorithms that are distortion-specific in nature. DIIVINE is based on natural scene statistics which govern the behavior of natural images. In this paper, we detail the principles underlying DIIVINE, the statistical features extracted and their relevance to perception and thoroughly evaluate the algorithm on the popular LIVE IQA database. Further, we compare the performance of DIIVINE against leading full-reference (FR) IQA algorithms and demonstrate that DIIVINE is statistically superior to the often used measure of peak signal-to-noise ratio (PSNR) and statistically equivalent to the popular structural similarity index (SSIM). A software release of DIIVINE has been made available online: "http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip" xmlns:xlink="http://www.w3.org/1999/xlink">http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip for public use and evaluation 
650 4 |a Journal Article 
650 4 |a Research Support, Non-U.S. Gov't 
650 4 |a Research Support, U.S. Gov't, Non-P.H.S. 
700 1 |a Bovik, Alan Conrad  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society  |d 1992  |g 20(2011), 12 vom: 30. Dez., Seite 3350-64  |w (DE-627)NLM09821456X  |x 1941-0042  |7 nnns 
773 1 8 |g volume:20  |g year:2011  |g number:12  |g day:30  |g month:12  |g pages:3350-64 
856 4 0 |u http://dx.doi.org/10.1109/TIP.2011.2147325  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 20  |j 2011  |e 12  |b 30  |c 12  |h 3350-64