Abstract
This paper presents a novel no-reference video quality assessment (VQA) model which is based on non-linear statistical modeling. In devised non-linear VQA model, an ensemble of neural networks is introduced, where each neural network is allocated to the specific group of video content and features based on artifacts. The algorithm is specifically trained to enable adaptability to video content by taking into account the visual perception and the most representative set of objective measures. The model verification and the performance testing is done on various MPEG-2 video coded sequences in SD format at different bit-rates taking into account different artifacts. The results demonstrate performance improvements in comparison to the state-of-the-art non-reference video quality assessment in terms of the statistical measures.
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
Preview
Unable to display preview. Download preview PDF.
References
Wolf, S., Pinson, M.: Video Quality Measurement Techniques. In NTIA Report 02-392 (2002)
Babu, R.V., Perkis, A.: An HVS-based no-reference Perceptual Quality Assessment of JPEG Coded Images Using Neural Networks. In: IEEE Int. Conference on Image Processing, vol. 1, pp. 433–436 (2005)
Wang, Z., Sheikh, H.R., Bovik, A.C.: No-reference Perceptual Quality Assessment of JPEG Compressed Images. In: IEEE International Conference on Image Processing, vol. 1, pp. 477–480 (2002)
Gillespie, W., Nguyen, T.: Classification of Video Sequences in MPEG Domain. In: Signal Processing for Telecomm. and Multimedia, ch. 6, vol. 27, pp. 71–86 (2005)
Quicker, U.N: PQM Block Artifact Detection Method. MICRONAS Intership Report (2008)
ITU-R Recommendation BT.500-11: Methodology for the Subjective Assessment of the Quality of Television Pictures. In: International Telecommunication Union, Geneva, Switzerland (2002)
Pokric, M., Kukolj, D., Pap, I., Lukic, N., Teslic, N., Temerinac, M., Marceta, Z., Zlokolica, V.: Video Quality Assessment on CELL. In: 4th International Workshop on Video Processing and Quality Metrics for Consumer Electronics, Scottsdale, USA (2009)
Zlokolica, V., Kukolj, D., Pokric, M., Lukic, N., Temerinac, M.: Content-Oriented Based No-Reference Video Quality Assessment for Broadcast. NEM Summit, Saint-Malo, France (2009)
Dietterich, T.G.: Ensemble Methods in Machine Learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity Creation Methods: A Survey and Categorization. Journal of Information Fusion 6(1) (2005)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan, NY (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kukolj, D.D., Pokrić, M., Zlokolica, V.M., Filipović, J., Temerinac, M. (2010). No-Reference Video Quality Assessment Design Framework Based on Modular Neural Networks. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15819-3_74
Download citation
DOI: https://doi.org/10.1007/978-3-642-15819-3_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15818-6
Online ISBN: 978-3-642-15819-3
eBook Packages: Computer ScienceComputer Science (R0)