Summary of Competitive Learning For Achieving Content-specific Filters in Video Coding For Machines, by Honglei Zhang et al.
Competitive Learning for Achieving Content-specific Filters in Video Coding for Machines
by Honglei Zhang, Jukka I. Ahonen, Nam Le, Ruiying Yang, Francesco Cricri
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Multimedia (cs.MM)
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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 This paper investigates a novel approach to adapting human-oriented video/image codecs for machine vision tasks by jointly optimizing content-specific post-processing filters. The proposed strategy, inspired by competitive learning principles and simulated annealing optimization techniques, assigns training samples to filters dynamically, allowing the winning filter to be optimized on each given sample. The authors evaluate their approach using the Versatile Video Coding (VVC) codec framework and demonstrate an improvement in BD-rate reduction for object detection and instance segmentation tasks on the OpenImages dataset. The results show a reduction from -41.3% to -42.3% for object detection and from -44.6% to -44.7% for instance segmentation, respectively, compared to independently trained filters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make video/image codecs work better for machines that can see. Currently, these codecs are designed for human use, but they don’t always do a good job when used by machine vision systems. The authors propose a new way of optimizing the post-processing filters in these codecs so they’re more suitable for machine vision tasks. They test their approach using a specific video codec framework and find that it improves performance on certain tasks, like object detection and instance segmentation. This could lead to better results when machines are used to analyze images. |
Keywords
» Artificial intelligence » Instance segmentation » Object detection » Optimization
