Abstract
This paper presents a new image database which provides images for evaluation and design of visual quality assessment metrics. It contains 1688 images, 8 reference images, 7 types of distortions per reference image and 30 distortions per type and reference. The distortion types address image errors arising in visual compositions of real and synthetic content, thus provide a basis for visual quality assessment metrics targeting augmented and virtual reality content. In roughly 200 subjective experiments over 17.000 evaluations have been gathered and Mean Opinion Scores for the database have been obtained. The evaluation of several existing and widely used quality metrics on the proposed database is included in this paper. The database is freely available, reproducible and extendable for further scientific research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15(11), 3440–3451 (2006)
Ponomarenko, N., et al.: A new color image database TID2013: innovations and results. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2013. LNCS, vol. 8192, pp. 402–413. Springer, Heidelberg (2013)
Kundu, D., Evans, B.L.: Full-reference visual quality assessment for synthetic images: a subjective study. In: Proceedings of IEEE International Conference on Image Processing (2015)
International Telecommunication Union, Bt.500-11, methodology for the subjective assessment of the quality of television pictures. ITU-R Recommendation, BT (2002)
Mantiuk, R.K., Tomaszewska, A., Mantiuk, R.: Comparison of four subjective methods for image quality assessment. Comput. Graph. Forum 31, 2478–2491 (2012). Wiley Online Library
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Mantiuk, R.K., Kim, K.J., Rempel, A.G., Heidrich, W.: HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions. ACM Trans. Graph. (TOG) 30, 40 (2011). ACM
Spearman, C.: The proof and measurement of association between two things. Am. J. Psychol. 15(1), 72–101 (1904)
Haccius, C., Herfet, T.: SSID-a synthetic image database (2016). http://www.nt.uni-saarland.de/SSID/. Accessed 29 Feb 2016
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Haccius, C., Herfet, T. (2016). An Image Database for Design and Evaluation of Visual Quality Metrics in Synthetic Scenarios. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-41501-7_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41500-0
Online ISBN: 978-3-319-41501-7
eBook Packages: Computer ScienceComputer Science (R0)