Summary of Perceptually Optimized Super Resolution, by Volodymyr Karpenko et al.
Perceptually Optimized Super Resolution
by Volodymyr Karpenko, Taimoor Tariq, Jorge Condor, Piotr Didyk
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG)
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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 The proposed approach is a perceptually inspired method for controlling the visual quality and efficiency of super-resolution techniques. It leverages the limitations of the human visual system to improve the efficiency of super-resolution methods by focusing computational resources on perceptually important regions. The technique is architecture-agnostic, making it compatible with various deep-learning based super-resolution methods. By dynamically guiding these methods according to the human’s sensitivity to image details, the approach reduces the computational resources spent on up-sampling visual content, resulting in improved efficiency without visible quality loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Super-resolution techniques can improve image and video quality, but they often waste computational resources because they process images and videos independently of their underlying content. This new approach uses a perceptual model to guide super-resolution methods based on how well humans can see details in the images. The method focuses on important regions where humans are most sensitive to detail, reducing the need for unnecessary processing. This leads to faster and more efficient processing without losing quality. |
Keywords
» Artificial intelligence » Deep learning » Super resolution
