Summary of Diversify, Contextualize, and Adapt: Efficient Entropy Modeling For Neural Image Codec, by Jun-hyuk Kim et al.
Diversify, Contextualize, and Adapt: Efficient Entropy Modeling for Neural Image Codec
by Jun-Hyuk Kim, Seungeon Kim, Won-Hee Lee, Dokwan Oh
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes a novel entropy modeling framework for neural codecs, aiming to improve the efficiency and effectiveness of entropy models. The authors argue that previous approaches have been limited by their reliance on a single type of hyper latent representation for forward adaptation. To address this limitation, they introduce a strategy of diversifying hyper latent representations, using two additional types of contexts along with the existing single type of context. This approach leverages more diverse contextual information, leading to improved rate-distortion performance across various bit-rate regions. Experimental results on popular datasets show that the proposed framework consistently outperforms state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how to make a special kind of computer model work better. Right now, these models are slow and not very good at compressing pictures or videos. The authors think this is because they only use one type of information to figure out what to do next. They propose using more types of information to help the model make better decisions. This should make the model faster and better at its job. The results show that their new approach works really well on popular datasets, like pictures taken with a camera. |
