DCVC-UF LD model evaluation setting
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 829
- Forks
- 138
- Avg merge
- 1d 2h
- Merged PRs (30d)
- 1
Description
Thanks for releasing the excellent work and the training/testing code!
We evaluated the DCVC-UF models. The RD performance of HT-L/S models aligns well with the results reported in the paper. For the LD model, we still observe a gap of approximately 0.5%–1.0% BD-rate compared with the reported result.
Could you please clarify the exact evaluation settings (such as command parameter, platform) used for the LD results in the paper? This information would help a lot.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by comparing the training/testing code with the LD results reported in the paper, focusing on command parameters and platform details. Reproduce the LD evaluation if possible and identify the source of the 0.5%–1.0% BD-rate gap. Done means the exact evaluation settings are clarified and documented.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- audio-video-rtc, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100