Compression
Compression and Information
Kraft inequality
See the Kraft Wiki page and
- Simple Proof for Kraft's Inequality (blog)
- Kraft's thesis
- McMillan article
- Kraft Code (by Shichao)
- Slides on Kraft 1
- Slides on Kraft 2
- Source Coding and Kraft Inequality (slides)
- Contribution of Kraft’s inequality to coding theory
- Varieties of Codes and Kraft Inequality
- A finite state version of the Kraft-McMillan theorem
- A dynamical systems proof of Kraft–McMillan inequality
- Permutation codes, source coding and Kraft inequalities
- A Kraft–type inequality for d–delay binary search codes
- On Unique Decodability
- Hierarchical Kraft and Hierarchical by Shen
Coding
Lossy compression
Lossy compression is the class of data compression methods that uses inexact approximations and partial data discarding to represent the content. These techniques are used to reduce data size for storing, handling, and transmitting content. This is opposed to lossless data compression which does not degrade the data.
- Lossy compression (wiki)
- List decoding
- On Lossy Compression
- Adaptive Coding and Prediction of Sources
- List decoding for noisy channels
- Complexity distortion theory
- The evolution of lossy compression
- Learning Better Lossless Compression Using Lossy Compression
- From Bi-immunity to Absolute Undecidability
- Singleton-type bounds for list-decoding and list-recovery
- List decoding: algorithms and applications
Sample Compression
Compression in practice
It is good to be in touch with both practical and theoretical aspects of compression.