© The Institution of Engineering and Technology
Image quality assessment (IQA) research strongly depends upon subjective experiments to provide ground truth to train and evaluate the IQA algorithms. These subjective experiments are cumbersome and expensive. An objective method based on human visual characteristics is proposed to generate the ground truth for distortion images. The proposed metric called Normalised Objective Distortion Score (NODS), using the logarithm of distortion parameter as the image quality score, is easily realised so that much manpower and time cost can be saved. The effectiveness of NODS has been analysed through experiments on five state-of-the-art IQA algorithms, and the result shows that the NODS is stable and can work as well as the subjective score when evaluating the performance of the IQA algorithms.
References
-
-
1)
-
3. Horita, Y., Shibata, K., Kawayoke, Y., Sazzad, Z.M.P.: ‘Mict image quality evaluation dabase 2000’, .
-
2)
-
8. Sheikh, H.R., Bovik, A.C.: ‘Image information and visual quality’, IEEE Trans. Image Process., 2006, 15, (2), pp. 430–444 (doi: 10.1109/TIP.2005.859378).
-
3)
-
1. Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: ‘TID2008 – a database for evaluation of full-reference visual quality assessment metrics’, Adv. Mod. Radioelectron., 2009, 10, pp. 30–45.
-
4)
-
7. Wang, Z., Simoncelli, E.P., Bovik, A.C.: ‘Multi-scale structural similarity for image quality assessment’. IEEE Asilomar Conf. Signals, Systems and Computers, Asilomar, CA, USA, November 2003, pp. 1398–1402.
-
5)
-
2. Callet, P.L., Autrusseau, F.: ‘Subjective equality assessment-ivc database, 2006’, .
-
6)
-
6. Stockham, T.G.: ‘Image processing in the context of a visual model’, Proc. IEEE, 1972, 60, (7), pp. 828–842 (doi: 10.1109/PROC.1972.8782).
-
7)
-
10. Moorthy, A.K., Bovik, A.C.: ‘A two-step framework for constructing blind image quality indices’, IEEE SPL, 2010, 17, (6), pp. 513–516.
-
8)
-
4. Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: ‘Live image quality assessment data set release 2’, .
-
9)
-
5. Larson, E.C., Chandler, D.M.: ‘Most apparent distortion: fullreference image quality assessment and the role of strategy’, J. Electron. Imaging, 2010, 19, (1), .
-
10)
-
9. Soundararajan, R., Bovik, A.C.: ‘RRED Indices: reduced reference entropic differencing for image quality assessment’, IEEE Trans. Image Process., 2012, 21, (2), pp. 517–526 (doi: 10.1109/TIP.2011.2166082).
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2013.1188
Related content
content/journals/10.1049/el.2013.1188
pub_keyword,iet_inspecKeyword,pub_concept
6
6