Summary of Machine Vision-aware Quality Metrics For Compressed Image and Video Assessment, by Mikhail Dremin (1) et al.
Machine vision-aware quality metrics for compressed image and video assessment
by Mikhail Dremin, Konstantin Kozhemyakov, Ivan Molodetskikh, Malakhov Kirill, Artur Sagitov, Dmitriy Vatolin
First submitted to arxiv on: 11 Nov 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 This paper presents a study on optimizing video compression algorithms for machine-vision processing in applications such as video surveillance and autonomous vehicles. The goal is to balance visual quality and file size while ensuring minimal human intervention. The authors explore how compression affects detection and recognition algorithms, proposing novel full-reference image/video-quality metrics tailored to machine vision. Experimental results show that these proposed metrics better correlate with machine-vision results for object, face, and license plate recognition tasks compared to existing metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers aim to create a better video-compression system that helps machines like computers see and understand videos without needing much human help. The goal is to make sure the compressed video still looks good while taking up less space on devices. To do this, they looked at how different compression levels affect machines’ ability to detect and recognize things in videos, such as objects, faces, or license plates. They also came up with new ways to measure how well a compressed video works for these tasks. |
