DCVC-UF LD model evaluation setting

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
35/100
Issue type
Documentation
Clarity
Needs clarification
Activity status
Quiet
Tech stack
python
Domain
audio-video-rtc, machine-learning

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.

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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.

Dominant language
Python
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Merged PRs (30d)
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