A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device
We propose a modular no-reference video quality prediction model for videos that are encoded with H.265/HEVC and VP9 codecs and viewed on mobile devices. The impairments which can affect video transmission are classified into two broad types depending upon which layer of the TCP/IP model they origin...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2017/8317590 |
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| author | Debajyoti Pal Vajirasak Vanijja |
| author_facet | Debajyoti Pal Vajirasak Vanijja |
| author_sort | Debajyoti Pal |
| collection | DOAJ |
| description | We propose a modular no-reference video quality prediction model for videos that are encoded with H.265/HEVC and VP9 codecs and viewed on mobile devices. The impairments which can affect video transmission are classified into two broad types depending upon which layer of the TCP/IP model they originated from. Impairments from the network layer are called the network QoS factors, while those from the application layer are called the application/payload QoS factors. Initially we treat the network and application QoS factors separately and find out the 1 : 1 relationship between the respective QoS factors and the corresponding perceived video quality or QoE. The mapping from the QoS to the QoE domain is based upon a decision variable that gives an optimal performance. Next, across each group we choose multiple QoS factors and find out the QoE for such multifactor impaired videos by using an additive, multiplicative, and regressive approach. We refer to these as the integrated network and application QoE, respectively. At the end, we use a multiple regression approach to combine the network and application QoE for building the final model. We also use an Artificial Neural Network approach for building the model and compare its performance with the regressive approach. |
| format | Article |
| id | doaj-art-a12a34805be4419ba30939a03a5c5b31 |
| institution | Kabale University |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-a12a34805be4419ba30939a03a5c5b312025-02-03T07:24:45ZengWileyAdvances in Multimedia1687-56801687-56992017-01-01201710.1155/2017/83175908317590A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile DeviceDebajyoti Pal0Vajirasak Vanijja1IP Communications Laboratory, School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok 10140, ThailandIP Communications Laboratory, School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok 10140, ThailandWe propose a modular no-reference video quality prediction model for videos that are encoded with H.265/HEVC and VP9 codecs and viewed on mobile devices. The impairments which can affect video transmission are classified into two broad types depending upon which layer of the TCP/IP model they originated from. Impairments from the network layer are called the network QoS factors, while those from the application layer are called the application/payload QoS factors. Initially we treat the network and application QoS factors separately and find out the 1 : 1 relationship between the respective QoS factors and the corresponding perceived video quality or QoE. The mapping from the QoS to the QoE domain is based upon a decision variable that gives an optimal performance. Next, across each group we choose multiple QoS factors and find out the QoE for such multifactor impaired videos by using an additive, multiplicative, and regressive approach. We refer to these as the integrated network and application QoE, respectively. At the end, we use a multiple regression approach to combine the network and application QoE for building the final model. We also use an Artificial Neural Network approach for building the model and compare its performance with the regressive approach.http://dx.doi.org/10.1155/2017/8317590 |
| spellingShingle | Debajyoti Pal Vajirasak Vanijja A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device Advances in Multimedia |
| title | A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device |
| title_full | A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device |
| title_fullStr | A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device |
| title_full_unstemmed | A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device |
| title_short | A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device |
| title_sort | no reference modular video quality prediction model for h 265 hevc and vp9 codecs on a mobile device |
| url | http://dx.doi.org/10.1155/2017/8317590 |
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