Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks
The aim of this paper is to present video quality prediction models for objective non-intrusive, prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over Universal Mobile Telecommunication Systems (UMTS) networks. In order to charac...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2010-01-01
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| Series: | International Journal of Digital Multimedia Broadcasting |
| Online Access: | http://dx.doi.org/10.1155/2010/608138 |
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| _version_ | 1849309285571362816 |
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| author | Asiya Khan Lingfen Sun Emmanuel Ifeachor Jose-Oscar Fajardo Fidel Liberal Harilaos Koumaras |
| author_facet | Asiya Khan Lingfen Sun Emmanuel Ifeachor Jose-Oscar Fajardo Fidel Liberal Harilaos Koumaras |
| author_sort | Asiya Khan |
| collection | DOAJ |
| description | The aim of this paper is to present video quality prediction models for objective non-intrusive, prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over Universal Mobile Telecommunication Systems (UMTS) networks. In order to characterize the Quality of Service (QoS) level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) and a second model based on non-linear regression analysis is proposed to predict the video quality in terms of the Mean Opinion Score (MOS). The objective of the paper is two-fold. First, to find the impact of QoS parameters on end-to-end video quality for H.264 encoded video. Second, to develop learning models based on ANFIS and non-linear regression analysis to predict video quality over UMTS networks by considering the impact of radio link loss models. The loss models considered are 2-state Markov models. Both the models are trained with a combination of physical and application layer parameters and validated with unseen dataset. Preliminary results show that good prediction accuracy was obtained from both the models. The work should help in the development of a reference-free video prediction model and QoS control methods for video over UMTS networks. |
| format | Article |
| id | doaj-art-18c4b64632224f7ca408f5a3c73ec0f3 |
| institution | Kabale University |
| issn | 1687-7578 1687-7586 |
| language | English |
| publishDate | 2010-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Digital Multimedia Broadcasting |
| spelling | doaj-art-18c4b64632224f7ca408f5a3c73ec0f32025-08-20T03:54:12ZengWileyInternational Journal of Digital Multimedia Broadcasting1687-75781687-75862010-01-01201010.1155/2010/608138608138Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS NetworksAsiya Khan0Lingfen Sun1Emmanuel Ifeachor2Jose-Oscar Fajardo3Fidel Liberal4Harilaos Koumaras5Centre for Signal Processing and Multimedia Communication, School of Computing, Communications and Electronics, University of Plymouth, Plymouth PL4 8AA, UKCentre for Signal Processing and Multimedia Communication, School of Computing, Communications and Electronics, University of Plymouth, Plymouth PL4 8AA, UKCentre for Signal Processing and Multimedia Communication, School of Computing, Communications and Electronics, University of Plymouth, Plymouth PL4 8AA, UKDepartment of Electronics and Telecommunications, University of the Basque Country (UPV/EHU), 48013 Bilbao, SpainDepartment of Electronics and Telecommunications, University of the Basque Country (UPV/EHU), 48013 Bilbao, SpainInstitute of Informatics and Telecommunications, NCSR Demokritos, 15310 Athens, GreeceThe aim of this paper is to present video quality prediction models for objective non-intrusive, prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over Universal Mobile Telecommunication Systems (UMTS) networks. In order to characterize the Quality of Service (QoS) level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) and a second model based on non-linear regression analysis is proposed to predict the video quality in terms of the Mean Opinion Score (MOS). The objective of the paper is two-fold. First, to find the impact of QoS parameters on end-to-end video quality for H.264 encoded video. Second, to develop learning models based on ANFIS and non-linear regression analysis to predict video quality over UMTS networks by considering the impact of radio link loss models. The loss models considered are 2-state Markov models. Both the models are trained with a combination of physical and application layer parameters and validated with unseen dataset. Preliminary results show that good prediction accuracy was obtained from both the models. The work should help in the development of a reference-free video prediction model and QoS control methods for video over UMTS networks.http://dx.doi.org/10.1155/2010/608138 |
| spellingShingle | Asiya Khan Lingfen Sun Emmanuel Ifeachor Jose-Oscar Fajardo Fidel Liberal Harilaos Koumaras Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks International Journal of Digital Multimedia Broadcasting |
| title | Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks |
| title_full | Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks |
| title_fullStr | Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks |
| title_full_unstemmed | Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks |
| title_short | Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks |
| title_sort | video quality prediction models based on video content dynamics for h 264 video over umts networks |
| url | http://dx.doi.org/10.1155/2010/608138 |
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