Summary of Optimal Video Compression Using Pixel Shift Tracking, by Hitesh Saai Mananchery Panneerselvam et al.
Optimal Video Compression using Pixel Shift Tracking
by Hitesh Saai Mananchery Panneerselvam, Smit Anand
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers aim to revolutionize video compression by leveraging machine learning (ML) techniques. Currently, video encoding/compression relies on hardcoded rules, which have been effective but only up to a certain point. To overcome this limitation, ML-based models have emerged in recent years and have outperformed several legacy codecs. The proposed approaches range from end-to-end video encoding using ML to replacing intermediate steps in legacy codecs with ML-enhanced methods. These innovations have the potential to significantly increase efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video compression is a crucial part of internet traffic, taking up around 85% of all online data. Right now, video encoding uses rules that were set long ago, which works well but only so far. New ideas are emerging using machine learning (ML) models to improve video compression. These ML-based models have already outdone some older ways of compressing videos. The researchers are exploring different approaches, from starting from scratch with an ML-only method to enhancing existing methods with ML. |
Keywords
» Artificial intelligence » Machine learning
