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In this paper, a metadata-based quality-estimation model is proposed for tile-based omnidirectional video streaming services, aiming to realize quality monitoring during service provision. In the tile-based omnidirectional video (ODV) streaming services, the ODV is divided into tiles, and the high-quality tiles and the low-quality tiles are distributed in accordance with the user's viewing direction. When the user changes the viewing direction, the user temporarily watches video with the low-quality tiles. In addition, the longer the time (delay time) until the high-quality tile for the new viewing direction is downloaded, the longer the viewing time of video with the low-quality tile, and thus the delay time affects quality. From the above, the video quality of the low-quality tiles and the delay time significantly impact quality, and these factors need to be considered in the quality-estimation model. We develop quality-estimation models by extending the conventional quality-estimation models for 2D adaptive streaming. We also show that the quality-estimation model using the bitrate, resolution, and frame rate of high- and low-quality tiles and that the delay time has sufficient estimation accuracy based on the results of subjective quality evaluation experiments.
Yuichiro URATA
NTT Corporation
Masanori KOIKE
NTT Corporation
Kazuhisa YAMAGISHI
NTT Corporation
Noritsugu EGI
NTT Corporation
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Yuichiro URATA, Masanori KOIKE, Kazuhisa YAMAGISHI, Noritsugu EGI, "Metadata-Based Quality-Estimation Model for Tile-Based Omnidirectional Video Streaming" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 5, pp. 478-488, May 2023, doi: 10.1587/transcom.2022EBP3109.
Abstract: In this paper, a metadata-based quality-estimation model is proposed for tile-based omnidirectional video streaming services, aiming to realize quality monitoring during service provision. In the tile-based omnidirectional video (ODV) streaming services, the ODV is divided into tiles, and the high-quality tiles and the low-quality tiles are distributed in accordance with the user's viewing direction. When the user changes the viewing direction, the user temporarily watches video with the low-quality tiles. In addition, the longer the time (delay time) until the high-quality tile for the new viewing direction is downloaded, the longer the viewing time of video with the low-quality tile, and thus the delay time affects quality. From the above, the video quality of the low-quality tiles and the delay time significantly impact quality, and these factors need to be considered in the quality-estimation model. We develop quality-estimation models by extending the conventional quality-estimation models for 2D adaptive streaming. We also show that the quality-estimation model using the bitrate, resolution, and frame rate of high- and low-quality tiles and that the delay time has sufficient estimation accuracy based on the results of subjective quality evaluation experiments.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2022EBP3109/_p
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@ARTICLE{e106-b_5_478,
author={Yuichiro URATA, Masanori KOIKE, Kazuhisa YAMAGISHI, Noritsugu EGI, },
journal={IEICE TRANSACTIONS on Communications},
title={Metadata-Based Quality-Estimation Model for Tile-Based Omnidirectional Video Streaming},
year={2023},
volume={E106-B},
number={5},
pages={478-488},
abstract={In this paper, a metadata-based quality-estimation model is proposed for tile-based omnidirectional video streaming services, aiming to realize quality monitoring during service provision. In the tile-based omnidirectional video (ODV) streaming services, the ODV is divided into tiles, and the high-quality tiles and the low-quality tiles are distributed in accordance with the user's viewing direction. When the user changes the viewing direction, the user temporarily watches video with the low-quality tiles. In addition, the longer the time (delay time) until the high-quality tile for the new viewing direction is downloaded, the longer the viewing time of video with the low-quality tile, and thus the delay time affects quality. From the above, the video quality of the low-quality tiles and the delay time significantly impact quality, and these factors need to be considered in the quality-estimation model. We develop quality-estimation models by extending the conventional quality-estimation models for 2D adaptive streaming. We also show that the quality-estimation model using the bitrate, resolution, and frame rate of high- and low-quality tiles and that the delay time has sufficient estimation accuracy based on the results of subjective quality evaluation experiments.},
keywords={},
doi={10.1587/transcom.2022EBP3109},
ISSN={1745-1345},
month={May},}
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TY - JOUR
TI - Metadata-Based Quality-Estimation Model for Tile-Based Omnidirectional Video Streaming
T2 - IEICE TRANSACTIONS on Communications
SP - 478
EP - 488
AU - Yuichiro URATA
AU - Masanori KOIKE
AU - Kazuhisa YAMAGISHI
AU - Noritsugu EGI
PY - 2023
DO - 10.1587/transcom.2022EBP3109
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2023
AB - In this paper, a metadata-based quality-estimation model is proposed for tile-based omnidirectional video streaming services, aiming to realize quality monitoring during service provision. In the tile-based omnidirectional video (ODV) streaming services, the ODV is divided into tiles, and the high-quality tiles and the low-quality tiles are distributed in accordance with the user's viewing direction. When the user changes the viewing direction, the user temporarily watches video with the low-quality tiles. In addition, the longer the time (delay time) until the high-quality tile for the new viewing direction is downloaded, the longer the viewing time of video with the low-quality tile, and thus the delay time affects quality. From the above, the video quality of the low-quality tiles and the delay time significantly impact quality, and these factors need to be considered in the quality-estimation model. We develop quality-estimation models by extending the conventional quality-estimation models for 2D adaptive streaming. We also show that the quality-estimation model using the bitrate, resolution, and frame rate of high- and low-quality tiles and that the delay time has sufficient estimation accuracy based on the results of subjective quality evaluation experiments.
ER -