Context Quantization based on Minimum Description Length and Hierarchical Clustering
The code length of a source can be reduced effectively by using conditional probability distributions in a context model. However, the larger the size of the context model, the more difficult the estimation of the conditional probability distributions in the model by using the counting statistics fr...
Main Authors: | Chen Hui, Chen Jianhua |
---|---|
Format: | Article |
Language: | English |
Published: |
EDP Sciences
2016-01-01
|
Series: | MATEC Web of Conferences |
Online Access: | http://dx.doi.org/10.1051/matecconf/20165601001 |
Similar Items
-
Minimum description length revisited
by: Peter Grünwald, et al.
Published: (2019-12-01) -
Spherical Minimum Description Length
by: Trevor Herntier, et al.
Published: (2018-08-01) -
Minimum Description Length Codes Are Critical
by: Ryan John Cubero, et al.
Published: (2018-10-01) -
A Study of Information Hiding Based on Hierarchical Clustering and Vector Quantization
by: Pei-Hsun Lin, et al.
Published: (2010) -
Model Selection via Minimum Description Length
by: Li, Li
Published: (2011)