Fractal Image Compression | Better performance of Fractal compression, approximate to the original at higher compression ratios

B. P. Santosh Kumar

ISBN 10: 3659757667 / ISBN 13: 9783659757662

Published by LAP LAMBERT Academic Publishing, 2015

About this Item

Fractal Image Compression | Better performance of Fractal compression, approximate to the original at higher compression ratios | B. P. Santosh Kumar | Taschenbuch | 76 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659757662 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 104261213

Bibliographic Details

• Title: Fractal Image Compression | Better performance of Fractal compression, approximate to the original at higher compression ratios
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2015
• Language: English
• ISBN 10: 3659757667
• ISBN 13: 9783659757662
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 131 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Fractal image presents both the mathematical ideas and the practical techniques involved in compressing images by means of fractals. With claims of compression ratios in excess of 10 000 to 1, fractal compression is certainly a promising new technology. However, the method is in fact quite controversial, with some people claiming it doesn't work well, while others claim it works wonderfully; as the technology is fast developing, but as yet unproven. At the moment, very little is known about fractal compression and what is known is difficult to ascertain. This paper attempts to clarify the key ideas behind the methodology; and includes an overview of the competition, which includes the JPEG standard and vector quantization. Fractal image compression relies on the fact that all real-world images are rich in affine redundancy; that is, under suitable affine transformations, large bits of real-world images look like smaller bits of the same real-world image. The basic idea behind the method is to express the image as a set of local iterated function systems (IFSs).

About the Author

B.P. Santosh Kumar obtained B.Tech in Electronics and Instrumentation Engineering from Sree Vidyanikethan Engineering College, Tirupathi. He also obtained M.Tech from Kerala University, Trivandrum. Presently he is working as Assistant Professor in Y.S.R. Engineering college of Yogi Vemana University, Proddatur