Low-complexity research for next-generation VVC standard and its neural network extension
| Project/Area Number |
21K17770
|
|---|
| Research Category |
Grant-in-Aid for Early-Career Scientists
|
| Allocation Type | Multi-year Fund |
|---|
| Review Section |
Basic Section 61010:Perceptual information processing-related
|
|---|
| Research Institution | Waseda University |
|---|
Principal Investigator |
孫 鶴鳴 早稲田大学, 理工学術院, 次席研究員 (90835886)
|
|---|
| Project Period (FY) |
2021-04-01 – 2024-03-31
|
| Project Status |
Granted (Fiscal Year 2022)
|
| Budget Amount *help |
¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2022: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2021: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
|
|---|
| Keywords | Video coding / VVC / Video coding for machine / Transform / Filter / Intra prediction / Neural network / Low complexity |
|---|
| Outline of Research at the Start |
Emerging video compression standard Versatile Video Coding (VVC) can double the compression ratio than the previous standard at the cost of high coding complexity. This research will reduce the complexity of VVC, and further enhance its coding gain by exploiting light yet efficient neural networks.
|
| Outline of Annual Research Achievements |
This research aims at reducing the complexity of the latest video compression standard Versatile Video Coding (VVC), from the aspect of both algorithm and architecture. For the algorithm, we developed a fast intra encoding for video coding for machine (VCM) framework, and a low complexity convolutional neural network filter. For the architecture, we proposed a reconfigurable multiple transform selection architecture for VVC.
|
| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
This year, we proposed a fast VVC intra coding method which aims at reducing machine-to-machine connection in the IoT society. The results have been presented in the CAS-society flagship conference ISCAS. In addition, we also developed a reconfigurable architecture for VVC transform. The related results are published in IEEE Transactions on Very Large Scale Integration Systems. Regarding the neural network extension beyond VVC, we developed a low-complexity filter and the results are published in the journal of IEEE Multimedia.
We also present several other international conferences.
|
| Strategy for Future Research Activity |
There are two targets. For the algorithm, we plan to continue the work on fast intra coding for machine. Currently, we only attempted the object detection and instance segmentation. We will evaluate and analyze more machine vision tasks in the future. For the architecture, we aim at designing an area-efficient architecture for VVC adaptive loop filter.
|
Report
(2 results)
Research Products
(20 results)
