PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Comparison of Effective Coverage Calculation Methods for Image Quality Assessment Databases

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Strony
307--313
Opis fizyczny
Bibliogr. 25 poz., rys., tab., wykr.
Twórcy
  • Poznan University of Technology, Poland
  • Poznan University of Technology, Poland
Bibliografia
  • [1] S. Winkler, Analysis of Public Image and Video Databases for Quality Assessment, Sel. Top. Signal Process. IEEE J., vol. 6, no. 6, pp. 616625, 2012.
  • [2] S. Winkler, ”Image and Video Quality Resources”, http://stefan.winkler.site/resources.html, [Mar, 2017].
  • [3] 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
  • [4] 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.
  • [5] 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.
  • [6] 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
  • [7] Patrick Le Callet, Florent Autrusseau, ”Subjective quality assessment IRCCyN/IVC database”, http://www.irccyn.ec-nantes.fr/ivcdb/ [Mar, 2017].
  • [8] 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].
  • [9] 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.
  • [10] 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.
  • [11] 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].
  • [12] 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].
  • [13] 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.
  • [14] 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).
  • [15] 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.
  • [16] 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.
  • [17] W. Sun, F. Zhou, Q. M. Liao. MDID: a multiply distorted image database for image quality assessment, Pattern Recognit. 61C (2017) pp. 153-168.
  • [18] 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. 10, pp. 30-45, 2009.
  • [19] 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.
  • [20] 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.
  • [21] ANSI T1.801.03 ”Digital transport of one-way video signals - parameters for objective performance assessment”, American National Standards Institute, New York, 1996.
  • [22] 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.
  • [23] M. Buczkowski, ”Measuring the effective coverage of the image databases”, Measurement Automation Monitoring, vol 63, 2017.
  • [24] 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.
  • [25] 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.
Uwagi
1. Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
2. Work funded by the Ministry of Science and Higher Education for the statutory activity of conducting research and development work and related tasks, contributing to the development of young scientists and doctoral students in 2017 under the project number 08/83/DSMK/4716.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b75e93d5-e4b3-447b-a5b8-5426bc663f7c
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.