| Project/Area Number |
17K06444
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Communication/Network engineering
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| Research Institution | Nihon University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
今村 幸祐 金沢大学, 電子情報通信学系, 准教授 (00324096)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 低遅延 / 動画像 / 符号化 / 動き予測 / 高位合成 / FPGA / 動画像符号化 / 高位設計 / 画像センシング / Full-HD / 予測方式 / 動画像転送 |
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| Outline of Final Research Achievements |
In this research, we applied various algorithms such as line-based motion prediction processing, dynamic range adaptive quantization, and context adaptive semi-fixed length coding based on the newly proposed line-based coding processing. As a result, this coding method realized ultra-low-latency performance of the order of 1/1000 microseconds compared to the conventional method. At the same time, it was verified that it has functional superiority as an encoding method such as high image quality performance and high compression rate of up to 20%. Furthermore, the superiority of the circuit amount and power consumption was verified by the architectural design and circuit implementation of this coding method. This result can be applied to application fields of real-time high-definition image sensing and ultra-low-latency transmission of in-vehicle equipment, medical equipment, and information equipment such as VR/AR.
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| Academic Significance and Societal Importance of the Research Achievements |
本課題である超低遅延動画像符号化方式は，今後の5Gをベースとする情報化社会において,ネットワークを介して人間の反応速度を遥かに超える低遅延動画像伝送を行うために必須の圧縮伝送技術である．特に車両の自動運転や遠隔医療およびVR/ARによるエンターテイメントなどの様々な応用分野に適用可能であり，今後の先端情報機器に幅広く活用できる．4K/8Kなどの超高精細画像センシング応用に向けてもこの基本技術の概念を適用することが可能であり，将来の高精細画像アプリケーションの発展に寄与できると考えられる．
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