Summary of Bit Rate Matching Algorithm Optimization in Jpeg-ai Verification Model, by Panqi Jia et al.
Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model
by Panqi Jia, A. Burakhan Koyuncu, Jue Mao, Ze Cui, Yi Ma, Tiansheng Guo, Timofey Solovyev, Alexander Karabutov, Yin Zhao, Jing Wang, 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)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents advancements in neural network (NN)-based image compression, outperforming traditional methods like JPEG-AI. NN models learn non-linear transforms, resulting in faster coding speeds on parallel devices and more compact bit representations. This has sparked interest from both scientific and industrial communities, leading to standardization efforts for JPEG-AI. A verification model is being developed, surpassing the VVC intra codec’s advanced performance. To assess the BD-rate performance of these models, bit rate matching is employed. However, the current state of the JPEG-AI verification model experiences slowdowns during bit rate matching, hindering optimal performance due to an unsuitable model. The proposed methodology offers a gradual algorithmic optimization for matching bit rates, resulting in a fourfold acceleration and over 1% improvement in BD-rate at the base operation point. At the high operation point, the acceleration increases up to sixfold. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a new way of compressing images using special computer models called neural networks. These models can learn from data and make good predictions, unlike old-fashioned methods that require human expertise. This has made people take notice, including scientists and companies that use these images. They want to standardize this new approach so it works well on many different devices. The paper also compares how well different models work at compressing images and shows that the new method is much faster than older ones. |
Keywords
* Artificial intelligence * Neural network * Optimization
