| 研究課題/領域番号 |
22K12299
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分62030:学習支援システム関連
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| 研究機関 | 会津大学 |
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研究代表者 |
Truong CongThang 会津大学, コンピュータ理工学部, 上級准教授 (40622957)
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| 研究期間 (年度) |
2022-04-01 – 2025-03-31
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| 研究課題ステータス |
交付 (2022年度)
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| 配分額 *注記 |
3,120千円 (直接経費: 2,400千円、間接経費: 720千円)
2024年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2023年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2022年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
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| キーワード | Quality of experience / Adaptive streaming / Online learning / Quality model / Media analysis / Multi-feature learning |
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| 研究開始時の研究の概要 |
We will first investigate and evaluate existing quality models and transmission methods of adaptive streaming. The models and methods will be improved and adapted for online learning, considering the presence of media objects. Then, solutions of monitoring and managing quality will be developed.
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| 研究実績の概要 |
In AY2022, we investigated problems of Quality of Experience (QoE) and video delivery for online learning. We evaluated of a large number of quality models for both PC and mobile users. Because online learning is mostly received via PC displays and has long durations, we found that just a few of quality models were appropriate for measuring the QoE at the learner’s side. Besides, the potential of multi-feature learning approach was studied for modeling. For video delivery, a new solution was proposed to send the same video to multiple users. Our solution employed scalable video coding and multicast mechanism to effectively deliver a video to many users in a session. We also investigated key issues and techniques in system implementation for QoE management for post-pandemic online learning.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
In the last year, we have successfully finished the investigation of both quality models and media delivery mechanism. The research has progressed rather smoothly as planned. It is found that existing quality models are still limited and need significant improvements. Thus, our current focus is on developing effective quality models to support both good perception and good semantic understanding for users.
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| 今後の研究の推進方策 |
In the AY2023, we will focus on the development of new quality models. The main approach is to take into account multiple features of media contents. Because in online learning, the quality of media contents may affect both the perception and understanding of users, the features will be extracted by considering both the perceptual aspect and semantic aspect of contents. New deep learning methods will be applied for efficient and effective deployment of quality models.
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