隨著數位科技的進步及網際網路的蓬勃發展，包含視訊、影像、聲音、文字等多媒體的相關產品與服務不斷推陳出新。惟多媒體應用動輒數十Mbytes以上的資料傳輸，以目前之儲存媒介、傳輸技術實無法輕易達成，故成為技術進步的嚴重瓶頸。因此，高效能的壓縮/解壓縮方法之研發，變得愈來愈重要。模糊邏輯(fuzzy logic)是近年來人工智慧應用中頗受歡迎且廣為使用的一個領域，模糊邏輯推理(fuzzy reasoning)更是極成功地用於工業上許多複雜、動態和不完整系統的控制。灰色系統理論則是一個新興的研究領域，而灰色預測也因其預測效果甚佳，廣泛用於經濟、工業上之預測問題。若能結合模糊推理與灰色預測，應能對壓縮領域開創一個新的思維。本論文主要即探討應用模糊推理與灰色預測以改善視訊影像壓縮方法的相關問題。
本文先介紹吾人所設計的一個可用於經常改變之環境的適應性模糊控制器(AFLC)。此適應性模糊控制器之特色包含：1)具調適能力，2)推論知識庫可隨環境的變化而快速調整，3)以動態歸屬函數產生器來產生各種不同的歸屬函數與4)具有縮窄、放寬、移動及增強歸屬函數的能力。由於AFLC需要極少的記憶體(50條模糊規則只需大約149 bytes的記憶空間)，卻能達到極大的運作彈性，而推論規則參數的符號化表示(symbolic format)更使得參數調整能在極短的時間內完成，故可用於環境經常改變的適應性控制中，達到快速且良好的控制效果。AFLC的VLSI晶片包含大約164K個電晶體，面積為 ，在非適應性模式(non-adaptive mode)下，每秒可完成490K次模糊推論，若在適應性模式下，則每秒可完成220K次模糊推論。
再者，我們也將模糊推理及灰色預測應用於算術編碼與區塊搜尋等視訊影像壓縮演算法的改進上。首先，我們發展出一個用於無失真壓縮的高效率適應性算術編碼法。此編碼法具備一個模糊灰色調整模式器(fuzzy-grey-tuning modeler)，該模式器使用模糊推理來適當地選擇輸入資料的機率變動步距(tuning step)，藉由此粗調過程，可決定輸入資料的粗估條件機率(conditional probability)。然後再根據先前輸入資料的狀態來建構灰微分方程式，以灰色預測來推估下一個輸入資料的可能值，並決定一個條件機率微調值－此為微調過程。結合粗調與微調過程，模糊灰色調整模式器能正確地估計輸入資料的可能機率，故編碼效率可大為提昇。實驗結果證明，此算術編碼法能對文字、影像與可執行資料檔案達到相當不錯的壓縮效率。此外，為了減少設計的複雜度並提高演算法的運算速度，我們使用查表法(table-look-up)來實現模糊灰色調整模式器，並提出有效的方法來解決來源終止(source ter-mination)與進位溢位(carry-over)等重大的實現問題。
區塊移動估計是降低視訊畫面間累贅的有效方法，其主要缺點則是移動向量的搜尋時間過長。針對此問題，我們也應用灰色預測來設計一個快速的灰色預測搜尋(GrPS)法，並提出實現此法的VLSI架構。此GrPS晶片包含大約54K個電晶體，面積為 ，能即時處理MPEG規格的影像。然後以GrPS為基礎，我們再提出一個更高效率的模糊搜尋方法(EFS)。實驗結果顯示，EFS可快速且正確地決定影像區塊的移動向量。因此，移動估計所產生的重建影像之品質甚佳，且所需的運算時間大量降低，易達到影像即時編碼的要求。由於在MPEG和H.263標準中，使用者可依其需要採用任何適當的搜尋方法來實現區塊移動估計，故所提的方法可直接用於這些視訊編碼標準及其相關應用中。目前，EFS已提出專利申請。

The fundamental characteristic of multimedia systems is that they incorporate con-tinuous media, such as voice, full-motion video, and graphics. Since the representation of audio, image, and video signals involves a vast amount of data, signal compression is in-dispensable. Fuzzy logic controllers (FLCs), based on expert systems and fuzzy logic, have been successfully applied to control vague, incomplete, complex, and ill-defined systems. By using the linguistic rules that capture the approximate and qualitative aspects of human knowledge, an FLC can infer desired output actions properly. Furthermore, grey theory is a new technology to solve the prediction problem of a time-varying non-linear system. Grey prediction has been widely and successfully used in many fields, such as economics, geography, medicine, weather, and automatic control. The integration of fuzzy logic and grey theory may yield a new dimension in compression research. The main objective of this dissertation is to use the concepts of fuzzy reasoning and grey pre-diction to develop some high-efficient compression methods.
In the dissertation, we first propose an adaptive fuzzy logic controller (AFLC) for the applications of the adaptive on-line control. Taking advantage of the adaptability pro-vided by a symbolic fuzzy rule format and the dynamic membership function generator, and the high-speed integration capability afforded by VLSI, the proposed AFLC can per-form an adaptive fuzzy inference process using various inference parameters dynamically and quickly. Its storage size of knowledge base is compact and occupies only 149 bytes for 50 fuzzy rules. With these features, the AFLC is capable of narrowing, widening and moving the antecedent and consequent membership functions, as well as amplifying and dampening the contribution weights of fuzzy control rules according to the direction from the external learning mechanism. The AFLC chip contains about 164K transistors and occupies a area. Operating at a clock rate of 33 MHz, the AFLC works very fast and yields an inference rate of 490K and 220K inferences/sec in the non-adaptive mode (with pipelining) and the adaptive mode (without pipelining), respec-tively.
Next, we present a novel adaptive arithmetic coding method for on-line lossless data compression. As far as arithmetic coding is concerned, the compression efficiency de-pends completely on accurate probability estimation. Since the characteristics of various digital signals bear a lot of uncertainty and are hard to be extracted, it is not easy to con-struct a good modeler that always provides accurate probability estimations for various types of source data. In the proposed arithmetic coding method, however, a smart fuzzy-grey-tuning modeler is developed. With the aid of fuzzy reasoning and grey predic-tion which dynamically determine the possible changes of probability, the coding method can estimate the conditional probabilities more precisely and efficiently. According to the experimental results, the proposed method works better than other compression methods for three types of source data: text, image and binary files. To reduce the implementation complexity and to prompt the coding throughput, we adopt the table-look-up method to construct the fuzzy-grey-tuning modeler. Furthermore, some implementation issues, such as source termination and carry-over, are also solved efficiently in the design.
Block motion estimation is an efficient method to reduce the temporal redundancy of video sequence. Due to the temporal and spatial correlation of image sequence, the motion vector of a reference block is highly related to the motion vectors of its adjacent blocks in the same image frame. By using the idea, we propose a fast grey prediction search (GrPS) algorithm and its VLSI architecture. With CMOS technology, GrPS chip needs a die size of mm2 with 54K transistors, and can provide suit-able solutions for MPEG2 ( in real time. Based on GrPS, we also present another fast search algorithm, named as the efficient fuzzy search (EFS) here. With the help of fuzzy reasoning, EFS works better than other search algo-rithms in terms of picture quality, accuracy, computational complexity and coding effi-ciency. Thus, it proves to be a good candidate for block motion estimation.

COVER
CONTENTS
CHAPTER 1 INTRODUCTION
1.1 Fuzzy Logic Controller
1.2 Arithmetic Coding
1.3 Block Motion Estimation
1.4 Organization of the Dissertatoin
CHAPTER 2 IMAGE AND VIDEO COMPRESSION
2.1 Introduction
2.2 Lossless versus Lossy Compression
2.3 Image Compression
2.4 Image Compression Standards
2.1.1 JBIG
2.4.2 JPEG
2.5 Video Compression
2.6 Video Compression Standards
2.6.1 MPEG
2.6.2 H.263
2.7 Concluding Remarks
CHAPTER 3 FUZZY REASONING AND GREY PREDICTION
3.1 Introduction
3.2 Fuzzy Sets and Fuzzy Logic
3.3 Fuzzy Logic Controller
3.3.1 Fuzzifier
3.3.2 Knowledge Base
3.3.3 Inference Logic
3.3.4 Defuzzifier
3.3.5 The FLC adopted in the dissertation
3.4 Grey Prediction
3.4.1 Grey Model
3.4.2 AGO and Inverse AGO
4.4.3 GM(1,1)
3.5 Concluding Remarks
CHAPTER 4 FUZZY LOGIC CONTROLLER AND ITS CHIP DESIGN
4.1 Introduction
4.2 Related Work
4.2.1 Previous work on Hardware FLCs
4.2.2 Previous work on Adptive Fuzzy Systems
4.3 An Adaptive Fuzzy Systems
4.3.1 Main Principles of an Adaptive Fuzzy System
4.3.2 The Proposed Adaptive Fuxzzy Hardware System
4.4 VLSI Architecture of AFLC
4.4.1 Knowledge Base
4.4.2 Dynamic Membership Function Generator
4.4.3 Input Fuzzifier
4.4.4 Inference-Processing Unit
4.4.5 Defuzzifier
4.4.6 Control Unit
4.4.7 Implementation and Performance
4.5 A Supervised Learning Algorithm for AFLC
4.5.1 Rule Generation Phase
4.5.2 Rule Tuning Phase
4.6 Applications of AFLC
4.6.1 Example 1-The Truck Backer-Upper Control Problem
4.6.2 Example 2-A Nonlinear Control Problem
4.6.3 Example 3-The Cart-Pole Balancing Problem
4.7 Concluding Remarks
CHAPTER 5 FUZZY-GREY-TUNING ARITHMETIC CODING
5.1 Introduction
5.2 Adaptive Arithmetic Coding
5.2.1 Main Principles of Arithmetic Coding
5.2.2 The Proposed Arithmetic Coding Method
5.2.2.1 The Coder
5.2.2.2 The FGM
5.3 The Design and Implementation of the Fuzzy-Grey Tuner
5.3.1 Design of Fuzzy Reasoning
5.3.2 Design of Grey Prediction
5.3.3 Implementation
5.4 Experimental Results
5.5 Concluding Remarks
CHAPTER 6 GREY PREDICTION BLOCK MOTION ESTIMATION AND ITS CHIP DESIGN
6.1 Introduction
6.2 Block-Matching Algorithm
6.3 The Grey Prediction Search Method
6.3.1 Finding the Predicted Motion Vector
6.3.2 Determining the Final Motion Vector
6.4 Hardware Implementation for GrPS
6.4.1 Memory Banks
6.4.2 Address Generators
6.4.3 Motion Vector Generator
6.4.4 Controller
6.4.5 VLSI Implementation
6.5 Concluding Remarks
CHAPTER 7 FUZZY REASONING BLOCK MOTION EXTIMATION AND ITS VLSI ARCHITECTURE
7.1 Introduction
7.2 EFS Algorithm
7.2.1 Finding the Predicted Motion Vector
7.2.2 Determining the Final Motion Vector
7.3 Measures for Performance Comparisons
7.3.1 Picture Quality
7.3.2 Accuracy
7.3.3 Computational Complexity
7.3.4 Coding Efficiency
7.4 Experimental Results and Comparisons
7.5 Hardware Implementation for EFS
7.6 Concluding Remarks
CHAPTER 8 CONCLUSIONS AND FUTURE WORK
8.1 Conclusions
8.2 Future Work
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