快速碎型演算法建構於嵌入式系統之實現

The Realization of Fast Fractal Compression Algorithm Implemented on Embedded System

鄭兆鈞(Chao-Chun Cheng)

指導教授 ： 龔志賢

長榮大學/管理學院/經營管理研究所(博)/碩士(2008年)

https://doi.org/10.6833/CJCU.2008.00171

摘要

在眾多的影像壓縮技術中，大致來說可分為無失真(Lossless Compression)與失真(Lossy Compression)影像壓縮，所謂的失真影像，即是當一張經過壓縮後的影像，無法完全還原(Irreversal)，也可稱為破壞性壓縮，碎型影像壓縮(Fractal Image Compression)就是其中一種，另外還有較為普遍的壓縮技術JPEG也是屬於失真性壓縮；雖然碎型影像壓縮是一種會失真的壓縮技術，但良好的壓縮率和較低的失真率，還是吸引許多學者研究的興趣，但碎型影像壓縮，最大的缺點就是必須花費大量的計算時間來進行編碼。然而，本研究提出一種利用減少比對的改進方法來降低壓縮的時間。此外，由於碎型影像壓縮的技術廣泛地受到歡迎，而有了資料比對、浮水印、醫學影像…等應用，因此衍生了應用於嵌入式系統(Embedded System)的想法。其主要用於手機或手持裝置上，讓這些裝置可以採用碎型壓縮系統的功能。因此，本研究的設計方法會以嵌入式系統的角度與開發方式來設計系統模型。

關鍵字

無失真壓縮 ； 失真壓縮 ； 破壞性壓縮 ； 碎型影像壓縮 ； 嵌入式系統

並列摘要

Among the image compression technology, broadly speaking can be divided into “Lossless Image Compression“ and “Lossy Image Compression”. The original image would be destroyed after compression such that it is named lossy image compression. Which is also known as destructive compression.
JPEG (Joint Photographic Coding Expert Group) is the most common image format in lossy image compression. Fractal is also a member of lossy compression group. The advantage of fractal compression is high compression ratio and lower distortion.
However the biggest drawback of fractal compression is long encodeing time. This paper proposes a soultion of low comparison rate compared method to reduces operational time. Moreover, the fractal is opplied to the watermark, data comparison and so on. The application of fractal compressions are widely used. Someday the fractal compression would be used on cell-phone or carriageable device. Therefore the notion that applications to embedded system been produced.

並列關鍵字

lossless compression ； lossy compression ； fractal compression ； embedded system

參考文獻

A. E. Jacquin(1992)。Image Coding Based on a Fractal Theory of Iterated Contractive Image Transformations。 IEEE Transaction on Image Processing。 vol. 1, No. 1, pp. 18-30。

Arnaud E. Jacquin(1992)。“Image Coding Base on a Fractal Theory of Iterated Contractive Image Transformations”。IEEE Transactions on Image Processing Vol. 1, No. 1, 18-30。

Nakayama T., Yakubo K.(2003)。 Fractal Concepts in Condensed Matter Physics。Solid-State Science。

Y. Fisher, Ed (1995)., Fractal Image Compression – Theory and Application, New York: Springer-Verlag。

張耀仁(民88)。LINUX核心研究篇。台北市：和碩科技文化叢書。