臺灣博碩士論文加值系統

碎形影像壓縮具有解析度獨立、快速解壓縮以及高壓縮率的優點。然而，碎形影像區塊編碼(fractal block coding：FBC)最大的問題在於計算值域方塊(range block)和定義域方塊(domain block)間的距離有非常高的複雜度。為了改善上述的缺點，本論文提出了兩個高效率的方法來改善碎形編碼過程。第一種方法是以餘弦定理(law of cosines：LOC)為基礎的快速碎形影像編碼(FBC-LOC)技術，經實驗模擬的結果，證明我們所提出之FBC-LOC架構可以非常快速且安全的消去不適合的定義域方塊，並保持與FBC相同的影像品質。第二種方法是運用區塊向量轉換(block vector transform：BVT)技術來作為FBC的前端訊號處理單元(BVT-FBC)，利用影像經BVT轉換後各頻帶內的係數保有極高關聯性且各頻帶係數間有極低關聯性的特性，來完成更有效率的碎形編碼系統，經實驗證明論文所提之BVT-FBC不僅比傳統的FBC有更快的編碼速度，而且在相同壓縮率下能完成更佳的解碼影像品質。

Fractal image coding (FBC) is a relatively new technique that has attracted much attention due to the advantages of resolution independence, fast decompression and high compression ratio. The major drawback of the fractal image compression is the high encoding complexity to find the best match between a range block and a large pool of domain blocks. In order to improve the drawback of the fractal image compression, two efficient methods are proposed to speed up the fractal encoding process.
The first method proposed is a fast fractal block coding based on the law of cosines (FBC-LOC). The number of domain blocks searched to find the best match for each range block is safely reduced by eliminating the ineligible domain blocks using the law of cosines. Simulation results show that the proposed algorithm can produce a completely identical fractal code to that of the exhaustive search in reduced time.
The second method proposed is to utilize a block vector transform (BVT) technique and employ it as a signal processing unit for fractal block coding (BVT-FBC). It is clear that the BVT is a vector-based signal processing operation which reduces correlation between vectors and leaves correlation between the components of each vector almost unchanged. Experimental results show that the proposed scheme can achieve faster coding and better image quality in the same bit rate than that of FBC.

ABSTRACT
FIGURE CAPTIONS
TABLE CAPTIONS
CHAPTER
1 INTRODUCTION
1.1 Why Needs Image Compression
1.2 Fractal Image Compression
2 FRACTAL IMAGE COMPRESSION
2.1 What Is Fractal Image Compression
2.2 Basic Theory
2.2.1 Local Self-Similarity
2.2.2 Iterated Function Systems
2.2.3 Local Iterated Function System
2.2.4 Resolution Independence
2.3 Implementation
3 FAST FRACTAL ENCODING USING THE LAW OF COSINES
3.1 The Law of Cosines
3.2 Sorted domain Pool via Variance
3.3 Partial Isometry Transform Search
3.4 The Law of Cosines Joint the PITS
3.5 Simulation Results
4 Block Vector Transform for Fractal Image Compression
4.1 Block Vector Transform (BVT)
4.2 BVT-FBC Coding System
4.3 Simulation Results
5 CONCLUSIONS
REFERENCES
APPENDIX

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