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
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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今後の研究の推進方策 |
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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