Kamali, Khalid (2005) Fractal video compression. [USQ Project]
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Abstract
With the rapid increase in the use of computers and internet, the demand for higher
transmission and better storage is increasing as well. One way to solve this problem is by
using compression, in which a small amount of data can represent the much larger
amount of original data.
This dissertation describes the different techniques for data (image-video) compression in
general and, in particular, the new compression technique called Fractal Image
Compression. Fractal image compression is based on self-similarity, where one part of
an image is similar to the other part of the same image.
The most significant aspect of this project is the development of color images using
fractal-based color image compression, since little work has been done previously in this
area. The results obtained show that the fractal-based compression for the color images
works as well as for the gray-scale images. Nevertheless, the encoding of the color
images takes more time than the gray-scale images.
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| Item Type: | USQ Project |
|---|---|
| Refereed: | No |
| Item Status: | Live Archive |
| Faculty/School / Institute/Centre: | Historic - Faculty of Engineering and Surveying - Department of Electrical, Electronic and Computer Engineering (Up to 30 Jun 2013) |
| Date Deposited: | 11 Oct 2007 00:26 |
| Last Modified: | 02 Jul 2013 22:33 |
| Uncontrolled Keywords: | data (image-video) compression, fractal image compression, fractal-based color image compression |
| Fields of Research (2008): | 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing 09 Engineering > 0906 Electrical and Electronic Engineering > 090699 Electrical and Electronic Engineering not elsewhere classified |
| Fields of Research (2020): | 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing 40 ENGINEERING > 4008 Electrical engineering > 400899 Electrical engineering not elsewhere classified |
| URI: | https://sear.unisq.edu.au/id/eprint/628 |
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