157 points by codexninja 1 year ago flag hide 13 comments
user1 1 year ago next
Fascinating! I've been looking for ways to reduce bandwidth usage with real-time video. Do you have any benchmarks against existing codecs like H0.264 or VP9?
dev4 1 year ago next
Yes, we ran some comparisons with H0.264 and VP9. ML-powered algorithm was about 20% more efficient saving bandwidth usage. Paper is open-sourced - link in description.
user2 1 year ago prev next
Have you tested this with heavy motion or high-frequency content? I wonder how it would handle those cases compared to more traditional codecs.
dev5 1 year ago next
In our testing, we observed that the ML-powered algorithm handled those cases better mainly due to its spatial and temporal prediction capabilities, which are more robust when it comes to motion.
user3 1 year ago prev next
This is great news! I'm working on a similar project using some techniques based on wavelets. Can't wait to test this out.
dev6 1 year ago next
Thanks for sharing! It's nice to see others working on improving video compression algorithms using advanced techniques. Would love to hear your thoughts on our approach too.
user7 1 year ago prev next
What's the performance of your ML model on real-time, low latency use cases? Can it take advantage of techniques like GPU offloading for faster computation?
dev8 1 year ago next
Yes, the ML model is optimized for low latency with the help of streamlined, efficient architecture, and tensor computation optimizations. CPU offloading on the server gives the required boost to the performance.
user9 1 year ago prev next
Awesome! I'd like to follow your progress. Keep us posted on any updates or further improvements.
user10 1 year ago prev next
This is impressive. Can you elaborate on the type of ML architectures used? Are there constraints on minimum bandwidths one user could have to effectively use this algorithm?
dev11 1 year ago next
The algorithm primarily utilizes a custom, lightweight CNN architecture for video compression alongside well-established compression techniques. Minimum bandwidth constraints are minimal due to the algorithm's improved compression capabilities.
user12 1 year ago prev next
Curious how this algorithm will perform on real-world use cases like your team's internal meetings, video conferencing, or streaming platforms?
dev13 1 year ago next
We've conducted tests on various real-world scenarios with promising results. With video conferencing apps, the savings were about 25-30% compared to H0.264 depending on the network configuration. For streaming services, results were highly dependent on implementation details and bitrate configurations.