Paper Explanation: Ballé2017 "End-to-end optimized Image Compression"
Published:Dec 16, 2025 13:40
•1 min read
•Zenn DL
Analysis
This article introduces a foundational paper on image compression using deep learning, Ballé et al.'s "End-to-end Optimized Image Compression" from ICLR 2017. It highlights the importance of image compression in modern society and explains the core concept: using deep learning to achieve efficient data compression. The article briefly outlines the general process of lossy image compression, mentioning pre-processing, data transformation (like discrete cosine or wavelet transforms), and discretization, particularly quantization. The focus is on the application of deep learning to optimize this process.
Key Takeaways
- •The paper focuses on using deep learning for image compression.
- •It addresses the importance of image compression in modern society.
- •The article outlines the general steps involved in lossy image compression.
Reference
“The article mentions the general process of lossy image compression, including pre-processing, data transformation, and discretization.”