GPU-based Transcoding Verification
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- Stars
- 7
- Forks
- 1
- PR merge metrics
- No merged PRs in 30d
Description
GPUs are 2-4x more efficient than CPUs at video transcoding. However, they use floating point calculation, and therefore don't fit into the current verification scheme. Instead of verifying the transcoding result based on a hash that requires bit-wise exact output, we can use a combination of video fingerprints, gop size, resolution, and video quality to verify a transcode output.
This also unlocks multi-threaded transcoding on CPUs.
Areas of research include:
* [Perceptual Hashing](https://en.wikipedia.org/wiki/Perceptual_hashing), video fingerprinting.
* Video quality measurement (should try [VMAF](https://github.com/Netflix/vmaf))
* Allowing multiple transcoding settings (and verification methods)
* Allowing broadcaster to pick verification method
Follow the discord discussion [here](http://discord.gg/Z4DXx63).
Contributor guide
No contributing guide indexed for this repository
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 reviewing the issue's proposed research areas: perceptual hashing, video fingerprinting, VMAF, GOP size, resolution, and video quality measurement. The issue names no repository files, tests, or entry points; done would require a defined verification design that supports GPU and multithreaded CPU transcoding.
Written by the indexing model from the issue text.
Assessment
- Domain
- audio-video-rtc
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100