Adaptive Reservation of Network Resources According to Video Classification Scenes
Video quality evaluation needs a combined approach that includes subjective and objective metrics, testing, and monitoring of the network. This paper deals with the novel approach of mapping quality of service (QoS) to quality of experience (QoE) using QoE metrics to determine user satisfaction limi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/6/1949 |
| _version_ | 1797541817999163392 |
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| author | Lukas Sevcik Miroslav Voznak |
| author_facet | Lukas Sevcik Miroslav Voznak |
| author_sort | Lukas Sevcik |
| collection | DOAJ |
| description | Video quality evaluation needs a combined approach that includes subjective and objective metrics, testing, and monitoring of the network. This paper deals with the novel approach of mapping quality of service (QoS) to quality of experience (QoE) using QoE metrics to determine user satisfaction limits, and applying QoS tools to provide the minimum QoE expected by users. Our aim was to connect objective estimations of video quality with the subjective estimations. A comprehensive tool for the estimation of the subjective evaluation is proposed. This new idea is based on the evaluation and marking of video sequences using the sentinel flag derived from spatial information (SI) and temporal information (TI) in individual video frames. The authors of this paper created a video database for quality evaluation, and derived SI and TI from each video sequence for classifying the scenes. Video scenes from the database were evaluated by objective and subjective assessment. Based on the results, a new model for prediction of subjective quality is defined and presented in this paper. This quality is predicted using an artificial neural network based on the objective evaluation and the type of video sequences defined by qualitative parameters such as resolution, compression standard, and bitstream. Furthermore, the authors created an optimum mapping function to define the threshold for the variable bitrate setting based on the flag in the video, determining the type of scene in the proposed model. This function allows one to allocate a bitrate dynamically for a particular segment of the scene and maintains the desired quality. Our proposed model can help video service providers with the increasing the comfort of the end users. The variable bitstream ensures consistent video quality and customer satisfaction, while network resources are used effectively. The proposed model can also predict the appropriate bitrate based on the required quality of video sequences, defined using either objective or subjective assessment. |
| first_indexed | 2024-03-10T13:21:03Z |
| format | Article |
| id | doaj.art-29dcfbf2e76c4792959de73de50562b3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:21:03Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-29dcfbf2e76c4792959de73de50562b32023-11-21T09:58:42ZengMDPI AGSensors1424-82202021-03-01216194910.3390/s21061949Adaptive Reservation of Network Resources According to Video Classification ScenesLukas Sevcik0Miroslav Voznak1Department of Telecommunications, VSB-Technical University of Ostrava, 708 00 Ostrava, Czech RepublicDepartment of Telecommunications, VSB-Technical University of Ostrava, 708 00 Ostrava, Czech RepublicVideo quality evaluation needs a combined approach that includes subjective and objective metrics, testing, and monitoring of the network. This paper deals with the novel approach of mapping quality of service (QoS) to quality of experience (QoE) using QoE metrics to determine user satisfaction limits, and applying QoS tools to provide the minimum QoE expected by users. Our aim was to connect objective estimations of video quality with the subjective estimations. A comprehensive tool for the estimation of the subjective evaluation is proposed. This new idea is based on the evaluation and marking of video sequences using the sentinel flag derived from spatial information (SI) and temporal information (TI) in individual video frames. The authors of this paper created a video database for quality evaluation, and derived SI and TI from each video sequence for classifying the scenes. Video scenes from the database were evaluated by objective and subjective assessment. Based on the results, a new model for prediction of subjective quality is defined and presented in this paper. This quality is predicted using an artificial neural network based on the objective evaluation and the type of video sequences defined by qualitative parameters such as resolution, compression standard, and bitstream. Furthermore, the authors created an optimum mapping function to define the threshold for the variable bitrate setting based on the flag in the video, determining the type of scene in the proposed model. This function allows one to allocate a bitrate dynamically for a particular segment of the scene and maintains the desired quality. Our proposed model can help video service providers with the increasing the comfort of the end users. The variable bitstream ensures consistent video quality and customer satisfaction, while network resources are used effectively. The proposed model can also predict the appropriate bitrate based on the required quality of video sequences, defined using either objective or subjective assessment.https://www.mdpi.com/1424-8220/21/6/1949bitratesubjective video quality assessmentobjective video quality assessmentspatial informationtemporal informationH.264/AVC |
| spellingShingle | Lukas Sevcik Miroslav Voznak Adaptive Reservation of Network Resources According to Video Classification Scenes Sensors bitrate subjective video quality assessment objective video quality assessment spatial information temporal information H.264/AVC |
| title | Adaptive Reservation of Network Resources According to Video Classification Scenes |
| title_full | Adaptive Reservation of Network Resources According to Video Classification Scenes |
| title_fullStr | Adaptive Reservation of Network Resources According to Video Classification Scenes |
| title_full_unstemmed | Adaptive Reservation of Network Resources According to Video Classification Scenes |
| title_short | Adaptive Reservation of Network Resources According to Video Classification Scenes |
| title_sort | adaptive reservation of network resources according to video classification scenes |
| topic | bitrate subjective video quality assessment objective video quality assessment spatial information temporal information H.264/AVC |
| url | https://www.mdpi.com/1424-8220/21/6/1949 |
| work_keys_str_mv | AT lukassevcik adaptivereservationofnetworkresourcesaccordingtovideoclassificationscenes AT miroslavvoznak adaptivereservationofnetworkresourcesaccordingtovideoclassificationscenes |