| Project/Area Number |
19J14620
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 国内 |
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| Review Section |
Basic Section 61010:Perceptual information processing-related
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| Research Institution | Waseda University |
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Principal Investigator |
CHENG ZHENGXUE 早稲田大学, 基幹理工学研究科, 特別研究員(PD)
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| Project Period (FY) |
2019-04-25 – 2021-03-31
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| Project Status |
Declined (Fiscal Year 2020)
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| Budget Amount *help |
¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2020: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2019: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | 画像圧縮 / 深層学習 / 高画質化 |
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| Outline of Research at the Start |
画像圧縮技術は、マルチメディアコンテンツの効率的な保存と伝送を可能とする基礎的な研究分野として知られている。従来の画像圧縮技術には、JPEG、JPEG2000、HEVCイントラがあるが、超高解像度、3D、360度やVRといったマルチメディアコンテンツのさらなる大容量化に伴い、より高効率な画像圧縮技術が求められている。本研究は、システム全体の設計して、深層学習を適用しエンドユーエンド学習を可能することに独創性があると言える。ユーザーの感じる主観画質の高画質化を目的とし、深層学習を活用した次世代の画像圧縮技術について研究開発を通して、画像圧縮技術のさらなる発展に貢献する。
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| Outline of Annual Research Achievements |
最近超高解像度、360度といったマルチメディアコンテンツのさらなる大容量化に伴い、より高効率な圧縮技術が求められている。令和1年度は、前述した目的を達成するために、従来の映像符号化標準に従い、提案するユーザーの感じる主観画質の高画質化を目的とし、CVPRとICIP国際学会で筆頭著者として発表を行うと共に、査読付き論文誌と令和２年度の国際学会ICASSPとCVPRへの採択が確定していた。
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
So far, the progress is conducted smoothly according to my plan. I have finished my target in the first year. And I have seperate my goals and future plan into two parts as followed.
Regarding deep learning based lossy image compression, I have reached a milestone, that is, reaching the performance of HEVC on both objective quality metrics and subjective quality, and the next plan for is to outperform VVC. Besides, some of our experiments have validate the possibility to achieve this target.
Regarding deep learning based lossy video compression, I have reached a milestone to achieve comparable performance with H.264. The next step is to outperform HEVC, or even VVC. Specially, my plan is to develop better P-frame prediction and B-frame prediction algorithms and incorporate them into inter-encoded video compression algorithms.
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| Strategy for Future Research Activity |
To promote the development of research, we are considering the following two strategies.
The first is to promote the learned image compression. I will develop a better entropy model by extending current single Gaussian to Gaussian mixture likelihoods. Second, I will enhance the network structure by consider some novel modules such as attention. The CVPR, a flagship meeting in the field of computer vision will have the event "Workshop and Challenge on Learned Image Compression” (CLIC) and I will participate in the challenge. Not only I can compare my results with research teams from other universities and companies, but also I could compare with conventional compression standards, because some hybrid methods are also submitted to this challenge.
The second is to enhance the performance of learned video compression. My plan is to enhance the overall performance module-by-module. Then what I can do include the better video prediction (P-frames) algorithms, better video interpolation algorithms (B-frames) and better network to encode residual and motion vectors etc. Based on the knowledge of learned image compression, I can extend the entropy models to videos and combine all these modules together. Finally, I can achieve better results than my current result.
Finally, through this research and development, we can contribute to the further development of compression technology.
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