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研究生:朱麒華
研究生(外文):Chyi-Hwa Chu
論文名稱:應用於顯示影像的再量化法研究
論文名稱(外文):On Image Requantization For Producing Display Images
指導教授:薛元澤薛元澤引用關係
指導教授(外文):Prof. Yuang-Cheh Hsueh
學位類別:博士
校院名稱:國立交通大學
系所名稱:資訊科學學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:1994
畢業學年度:82
語文別:英文
論文頁數:130
中文關鍵詞:影像再量化影像強化影像壓縮基數分配
外文關鍵詞:image requantizationimage enhancementimage compression
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影像必須經過再量化的處理才能顯示在只能處理少許灰度值或色彩的低階
繪圖設備上。本篇論文包含彩色與灰度影像的再量化處理。第一、我們介
紹新的量化技巧稱為基數分配。當基數分配作為灰度影像再量化法時,相
對於常用的序顫法、誤差分散法、與斑點分散法有許多優點。在本文中,
我們提出一種相似量度叫作量化誤差曲線,來強調基數分配法其中一項優
點。第二、我們更進一步地拓展基數分配在彩色影像再量化的應用,由此
來克服個人電腦顯示彩色影像的困難。第三、有時候為了能使細微的部份
能顯現清楚,量化影像需進一步的強化。在本篇論文中,我們提出一些新
的演算法來強化量化過的影像。首先我們引薦反模糊的觀念來改善誤差分
散法。接著,我們為所有量化演算法提出改進,使它們能夠捕捉灰度量化
影像的細微部份。最後,我們也為彩色量化影像提出一套新的強化方法。
第四,也就是本文的最後一部份,我們應用基數分配技巧來壓縮影像。基
數分配法吸引人的優點中有一項是能夠產生序顫紋理,此紋理非常適用在
影像壓縮上。在本文中,我們藉由基數分配來壓縮影像,並與多階序顫法
比較。
An image must be re-quantized when it will be displayed on a
low level graphics device, i.e., a graphics device that can
handle only few gray levels or colors. This thesis addresses
the problems of both grayscale and color image
requantizations. First, we introduce a new technique called
cardinality distribution. As a grayscale image requantization
method, cardinality distribution has many advantages over the
popular algorithms, ordered dither, error diffusion, and dot
diffusion algorithms. To classify one of the advantages for
cardinality distribution, we will propose a similarity measure,
called the quantization error curve. It is useful to measure
the similarity of requantized images. Second, we extend the
application of cardinality distribution to color image
requantization. When cardinality distribution is applied to
display color images, it can overcome the difficulties of
displaying color images on personal computers. Third, for
visual effect, requantized images are sometimes enhanced. In
this thesis, we propose some enhancement techniques for
requantized images. At first, we employ the concept of
defuzzification to improve the error diffusion algorithm. Next
we propose a modification for all quantization algorithms to
capture the sharp details in the requantized grayscale images.
Finally, we propose a new enhancement algorithm for requantized
color images. At last, we employ cardinality distribution to
compress images. A very interesting property of cardinality
distribution is that it can produce dithering patterns which
are suitable for image compression. In this thesis, we
compress images by cardinality distribution and compare the
results with those by multilevel dither technque.
COVER
ABSTRACT (IN CHINESE)
ABSTRACT
ACKNOWLEDGEMENTS
CONTENTS
LIST OF TABLES
LIST OF FIGURES
CHAPTER 1: INTRODUCTION
1.1 Motivation
1.2 Previous Works on Grayscale Image Requantization
1.3 Previous Works on Color Quantization
1.4 Contributions of This Thesis
1.5 Thesis Scope
CHAPTER 2: GRAYSCALE IMAGE REQUANTIZATION
2.1 Introduction
2.2 Ordered Dither Technique
2.3 Error Diffusion Algorithms
2.4 Dot Diffusion
2.5 Cardinality Distribution
2.6 A Similar Measurement: Quantization Error Curve
2.7 Experimental Results And Discussions
CHAPTER 3: COLOR IMAGE REQUANTIZATION
3.1 Motivation
3.2 Median Cut Algorithm
3.3 Color Image Display by Cardinality Distribution
3.3.1 Uniform Quantization with Fixed Color Palette
3.3.2 Uniform Quantization with Adaptive Color Palette
3.4 Color Quantization with Prequantization by Cardinality Distribution
3.5 Discussions
3.5.1 256-color Images
3.5.2 16-color Images
3.5.3 Prequantization
CHAPTER 4: ENHANCEMENT OF QUANTIZED IMAGES
4.1 Motivation
4.2 Error Diffusion with the Concept of Defuzzification
4.2.1 Description of Proposed Algorithm
4.2.2 Discussions
4.3 An Adaptive Enhancement Algorithm
4.3.1 Description of Proposed Algorithm
4.3.2 Discussions
4.4 Enhancement for Color Quantized Images
CHAPTER 5: CONCLUSIONS AND FUTURE WORKS
5.1 Conclusions
5.2 Future Works
REFERENCES
APPENDIX: IMAGE COMPRESSION BY CARDINALITY DISTRIBUTION
A.1 Introduction
A.2 Gray Level Reduction, Dithering Pattern Compression and Decompression
A.3 Gray Level Expansion by Cardinality Distribution
A.4 Discussions
A.4.1 Visual Test and Error Analysis
A.4.2 Card-Distribution vs. Multi-level dither
VITA
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