Rateless lossy compression via the extremes
Abstract/Contents
- Abstract
- In the era of Big Data, the volume of data grows rapidly, and compression is becoming an increasingly important issue. Information theory, in particular, approaches this problem mathematically to find optimum compression algorithms. Such works often focus more on optimality of compression rate but tacitly ignore the complexity of algorithms. On the other hand, in the real world, various compression algorithms are developed for each different application. These algorithms provide low complexity but cannot guarantee the optimum compression rate. In this work, we try to achieve both the low complexity and optimality of the rate. More precisely, a new technique using successive refinement is proposed to reduce the complexity of algorithms that achieve the optimum compression rate.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2015 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | No, Albert |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Weissman, Tsachy |
| Thesis advisor | Weissman, Tsachy |
| Thesis advisor | El Gamal, Abbas A |
| Thesis advisor | Goldsmith, Andrea, 1964- |
| Advisor | El Gamal, Abbas A |
| Advisor | Goldsmith, Andrea, 1964- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Albert No. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2015. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2015 by Albert No
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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