Name
Title | ||
|---|---|---|
A Novel Scheme to Improve Lossless Image Coders by Explicit Description of Generative Model Classes. |
Abstract | ||
|---|---|---|
In this study, we propose a novel scheme for systematic improvement of lossless image compression coders from the point of view of the universal codes in information theory. In the proposed scheme, we describe a generative model class of images as a stochastic model. Using the Bayes codes, we are able to construct a lossless image compression coder which is optimal under the Bayes criterion for a model class described appropriately. Since the compression coder is optimal for the assumed model class, we are able to focus on the expansion of the model class. To validate the efficiency of the proposed scheme, we construct a lossless image compression coder which achieves approximately 19.7% reduction of average coding rates of previous coders. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Information Theory | Journal |
Volume | Citations | PageRank |
abs/1802.04499 | 0 | 0.34 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuta Nakahara | 1 | 0 | 0.68 |
| Toshiyasu Matsushima | 2 | 97 | 32.76 |