Comparison of Effective Coverage Calculation Methods for Image Quality Assessment Databases

Authors

  • Mateusz Buczkowski Poznan University of Technology
  • Ryszard Stasiński Poznan University of Technology

Abstract

This article provides a comparison of a three methods that can be used for calculating effective coverage of image quality assessment database. The aim of this metric is to show how well the database is filled with variety of images. For
each image in the database the Spatial Information (SI) and Colorfulness (CF) metric is calculated. The area of convex hull containing all the points on SI x CF plane is indication of total coverage of the database, but it does not show how efficiently this area is utilized. For this purpose an effective coverage was introduced. An analysis is performed for 16 databases - 13 publicaly available and 3 artificial created for the purpose of showing advantages of the effective coverage.

References

S. Winkler, Analysis of Public Image and Video Databases for Quality

Assessment, Sel. Top. Signal Process. IEEE J., vol. 6, no. 6, pp. 616625,

S. Winkler, ”Image and Video Quality Resources”,

http://stefan.winkler.site/resources.html, [Mar, 2017]

Liu X., Pedersen M., Hardeberg J.Y. (2014) CID:IQ A New Image

Quality Database. In: Elmoataz A., Lezoray O., Nouboud F., Mammass

D. (eds) Image and Signal Processing. ICISP 2014. Lecture Notes in

Computer Science, vol 8509. Springer, Cham

E. C. Larson and D. M. Chandler, ”Most Apparent Distortion: Full-

Reference Image Quality Assessment and the Role of Strategy,” Journal

of Electronic Imaging, 19 (1), March 2010.

Silvia Corchs, Francesca Gasparini, Raimondo Schettini, No Reference

Image Quality classification for JPEG-Distorted Images, In Digital Signal

Processing, volume 30, pp. 86-100, Elsevier, 2014.

Silvia Corchs, Francesca Gasparini, Raimondo Schettini , Noisy Images-

JPEG Compressed: Subjective and Objective Image Quality Evaluation,

In Image Quality and System Performance XI, volume 9016, pp. 90160-,

SPIE, 2014

Patrick Le Callet, Florent Autrusseau, ”Subjective quality assessment IRCCyN/

IVC database”, http://www.irccyn.ec-nantes.fr/ivcdb/ [Mar, 2017]

H.R. Sheikh, Z.Wang, L. Cormack and A.C. Bovik,

”LIVE Image Quality Assessment Database Release 2”,

http://live.ece.utexas.edu/research/quality [Mar, 2017]

H.R. Sheikh, M.F. Sabir and A.C. Bovik, ”A statistical evaluation of

recent full reference image quality assessment algorithms”, IEEE Transactions

on Image Processing, vol. 15, no. 11, pp. 3440-3451, Nov. 2006.

Z. Wang, A.C. Bovik, H.R. Sheikh and E.P. Simoncelli, ”Image quality

assessment: from error visibility to structural similarity,” IEEE Transactions

on Image Processing , vol.13, no.4, pp. 600- 612, April 2004.

D. Ghadiyaram and A.C. Bovik, ”Massive Online Crowdsourced Study

of Subjective and Objective Picture Quality,” IEEE Transactions on Image

Processing, accepted arXiv 2015 [arXiv]

D. Ghadiyaram and A.C. Bovik, ”LIVE In the

Wild Image Quality Challenge Database,” Online:

http://live.ece.utexas.edu/research/ChallengeDB/index.html [Mar, 2017]

Dinesh Jayaraman, Anish Mittal, Anush K. Moorthy and Alan C. Bovik,

Objective Quality Assessment of Multiply Distorted Images, Proceedings

of Asilomar Conference on Signals, Systems and Computers, 2012.

Lina Jin, Joe Yuchieh Lin, Sudeng Hu, Haiqiang Wang, Ping Wang,

Ioannis Katsavounidis, Anne Aaron and C.-C. Jay Kuo. Statistical Study

on Perceived JPEG Image Quality via MCL-JCI Dataset Construction and

Analysis. Electronic Imaging (2016), the Society for Imaging Science and

Technology (IS&T).

Sudeng Hu, Haiqiang Wang and C.-C. Jay Kuo, A GMM-based stair

quality model for human perceived JPEG images, IEEE International

Conference on Acoustic, Speech and Signal Processing, Shanghai, China,

March 20-25, 2016

Joe Yuchieh Lin, Lina Jin, Sudeng Hu, Ioannis Katsavounidis, Anne

Aaron and C.-C. Jay Kuo. Experimental Design and Analysis of JND

Test on Coded Image/Video. SPIE Optical Engineering+ Applications.

International Society for Optics and Photonics, 2015

W. Sun, F. Zhou, Q. M. Liao. MDID: a multiply distorted image database

for image quality assessment, Pattern Recognit. 61C (2017) pp. 153-168.

N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, F.

Battisti, ”TID2008 - A Database for Evaluation of Full-Reference Visual

Quality Assessment Metrics”, Advances of Modern Radioelectronics, Vol.

, pp. 30-45, 2009.

A.Zaric, N.Tatalovic, N.Brajkovic, H.Hlevnjak, M.Loncaric, E.Dumic,

S.Grgic, ”VCL@FER Image Quality Assessment Database”, AUTOMATIKA

Vol. 53, No. 4, pp. 344354, 2012

K. Ma et al., ”Waterloo Exploration Database: New Challenges for

Image Quality Assessment Models,” in IEEE Transactions on Image

Processing, vol. 26, no. 2, pp. 1004-1016, Feb. 2017.

ANSI T1.801.03 ”Digital transport of one-way video signals - parameters

for objective performance assessment”, American National Standards

Institute, New York, 1996

D. Hasler, S. Susstrunk, ”Measuring colourfulness in natural images”,

Proc. SPIE Human Vision and Electronic Imaging vol. 5007, Santa Clara,

CA, January 21-24, 2003, pp.87-95

M. Buczkowski, ”Measuring the effective coverage of the image

databases”, Measurement Automation Monitoring, vol 63, 2017

M. Buczkowski, R. Stasiski, ”Effective coverage as a new metric for image

quality assessment databases comparison,” International Conference

on Systems, Signals and Image Processing (IWSSIP), Poznan, 2017

B. Delaunay, Sur la spheere vide. A la meemoire de Georges Voronoi,

Bulletin de lAcademie des Sciences de lURSS. Classe des sciences

mathematiques et na, no. 6, pp. 793800, 1934

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Published

2018-07-20

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Section

Image Processing