Summary of Bit Distribution Study and Implementation Of Spatial Quality Map in the Jpeg-ai Standardization, by Panqi Jia et al.
Bit Distribution Study and Implementation of Spatial Quality Map in the JPEG-AI Standardization
by Panqi Jia, Jue Mao, Esin Koyuncu, A. Burakhan Koyuncu, Timofey Solovyev, Alexander Karabutov, Yin Zhao, Elena Alshina, Andre Kaup
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 explores the development of neural network-based image compression codecs, particularly focusing on JPEG-AI, a standardization effort aimed at enhancing the efficiency and quality of compressed images. The authors utilize neural networks to create compact bit representations, achieving over 10% better performance compared to VVC intra at base operation point. The study highlights the importance of flexible bit distribution in the spatial domain, which is achieved through the proposed spatial bit allocation method. Furthermore, applying the VCC bit distribution strategy can result in a maximum gain of 0.45 dB in PSNR-Y. This research has significant implications for the standardization and industrial communities, as it paves the way for more efficient and high-quality image compression techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have lots of pictures on your phone or computer, and you want to compress them so they take up less space. One way to do this is by using special codes called neural networks. These codes can make the images smaller and faster to send or store. Researchers are very interested in these codes because they work well and could become a standard for how we compress images. In this study, scientists compared two different methods of compressing images: one using neural networks and another using a traditional method called VVC intra. They found that the neural network method works better and can make the images look slightly clearer. This discovery could lead to new ways of compressing images in the future. |
Keywords
* Artificial intelligence * Neural network
