在最近幾年中裡小波轉換(wavelet transform)以達到成熟完善的階段，而且成為許多影像壓縮不可缺少的技術。對於整數可逆小波轉換來說，更提供了無失真以及有損的影像壓縮基礎。我們改進原本JPEG-LS並無漸進式傳輸的缺點，並結合小波轉換提供一個漸進式影像壓縮的方法。
在本篇論文中，我們研究在目前影像壓縮中佔有重要功能的漸進式傳輸以及漸進式傳輸的理論基礎，並說明實際利用濾波器的作法。在對影像作小波轉換以後，系統會將所得到的小波係數傳送到我們所提出的方法 I-LOCO-I，解碼後會產生一張near-lossless的影像，且壓縮比比JPEG-LS來的高及很好的品質，PSNR值為51.6。
另一個可以達到漸進式方法為在做I-LOCO-I前先做量化產生一張preview的影像再來near-lossless以及lossless分別會傳送到接收端。最後，利用I-LOCO-I結合wavelet我們可以得到比原本JPEG-LS高15%的壓縮比，若不使用wavelet的話就可以有大於約23%的壓縮比。

In recent years wavelet image compression technology has rapidly reached its maturity, and become a method of choice for still image compression. Integer reversible wavelet transforms allow both lossless and lossy decoding using a single bitstream. We present a new fully progressive image coder and investigate the lossless and near-lossless performance of these transforms in the propose coder.
In this thesis, we studied the architecture and theories of the more and more important function, progressive transmission, in the image compression. The theory of wavelet transform is discussed and the implementation method using filter is explained. After doing wavelet transform, the codec transmit the wavelet coefficient to our proposed method call I-LOCO-I to compression.
Here, the proposed I-LOCO-I near-lossless compression is provided by a simple quantization of the image prior to lossless or near-lossless coding and with high compression ratio and has fairly good PSNR 51.6. It will first present a preview image after error correction. That compression ratio of I-LOCO-I with Doubaches 5/3 wavelet transform is better than LOCO-I about 15%. If I-LOCO-I without wavelet transform, its compression ratio can achieve 23%.

中文摘要 ……………………………………………………………………… i
Abstract …………………………………………………………………………. ii
Contents ………………………………………………………………………… iii
List of figures …………………………………………………………………… iv
Chapter 1 Introduction ………………………………………………………………..1
1.1 Motivation and Related Researches………………………………………...1
1.3 Thesis Organization ………………………………………………………2
Chapter 2 Wavelet-based image Compression…………………………………….…3
2.1 Introduction to Wavelet Transform…………………………………………3
2.1.1 Basic Operations…………………………………………………….5
2.1.2 DWT for Image Processing…………….…………………………..17
2.2 Progressive Image Compression Algorithm……………………………20
2.2.1 Embedded Zerotree Wavelet (EZW) ………………………………20
2.2.2 Set Partitioning Hierarchical Tree (SPIHT) ……………………….22
2.2.3 JPEG 2000………………………………………………………….24
2.2.4 P-JPEG based on DCT……………………………………………..25
2.3 Comparison Methodology……………………………………………..27
Chapter 3 Prediction Method for Image Compression…………………………….29
3.1 LOCO-I/JPEG-LS Codec………………………………………………….29
3.1.1 Introduction to JPEG-LS ………………………………………..29
3.1.2 JPEG-LS Main Algorithm…………………………………………31
Chapter 4 Proposed Scheme of Progressive JPEG-LS………………………………36
4.1 A Hybrid method of IDWT and LOCO-I…………………………………36
iii
4.1.1 The Problem of Combining Wavelet with LOCO-I………………36
4.1.2 Index LOCO-I ( I-LOCO-I ) Predicted Scheme………………… 37
4.1.3 SNR Scalability Layers Plus I-LOCO-I………………………… 40
4.2 Simulation Result And Performance Analysis………………… ……….. 43
4.2.1 Experiment Result……………………………………………….. 44
Chapter 5 Conclusion and Future Research…………………………………………47
Reference……………………………………………………………………………..48

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