雖然以匹配搜尋演算法和位元平面編碼為基礎的細微可調適性視訊編碼架構已被證明在低位元速率下比離散餘弦轉換為基礎的細微可調適性視訊編碼架構擁有更高的峰值訊號雜訊比，但以位元率-失真最佳化為觀點，其編碼程序可能不是最具效率的作法。我們藉由將畫面分割為許多區塊並依位元率-失真平面之斜率將各區塊間嵌入式位元字串重組至數個畫質分層的作法，提出位元率-失真最佳化之細微可調適性視訊壓縮演算法，並於實驗結果說明我們對所提出的演算法和細微可調適性匹配搜尋視訊壓縮演算法之間的比較。

Although fine granularity scalability (FGS) video coding based on matching pursuits and bit-plane coding have been proven to have better coding efficiency than discrete-cosine-transform-based FGS at low bit rates, it might not be the most efficient method in terms of rate-distortion optimization (RDO). We propose a rate-distortion optimization FGS video coding by dividing a frame into blocks to generate block-based embedded bit-streams and reorganize the bit-streams into several quality layers according to the rate-distortion slopes. The comparison between FGS matching pursuit video coding and RDO-FGS matching pursuit video coding is shown in the experimental results.

章節目錄

誌謝 3
中文摘要 4
英文摘要 5
章節目錄 6
圖目錄 8

第一章
緒論 10
1.1 前言 10
1.2 研究背景 11
1.3 研究動機 13
1.4 論文結構 14

第二章
文獻回顧 15
2.1 匹配搜尋演算法 15
2.2 以匹配搜尋演算法為基礎的細微可調適性 16
2.2.1 連續近似量化位元平面 17
2.2.2 原子位置的四分樹表示與預測演算法 17
2.2.3 累進式原子編碼演算法 20
2.2.4 以匹配搜尋演算法為基礎的細微可調適性視訊編解碼架構 21
2.3 JPEG2000位元率-失真最佳化 24
2.3.1 第一階層：嵌入式區塊編碼 25
2.3.2 第一階層：優化截斷、後壓縮位元率-失真最佳化 26
2.3.3 第二階層：畫質分層 29

第三章
位元率-失真最佳化之細微可調適性 31
3.1 概要 31
3.2 評估位元率-失真平面之斜率 33
3.2.1 以區塊為單位的嵌入式位元字串 33
3.2.2 合適的斜率 35
3.2.3 失真 38
3.2.4 位元率估測 40
3.2.5 PCRD應用於匹配搜尋視訊編碼 41
3.3 指定畫質分層 42
3.3.1 畫質分層和掃瞄次序 42
3.3.2 標頭檔 46
第四章
效能評估與比較 48
第五章
結論與未來展望 55

參考文獻 56

參考文獻

[1] O. Al-Shaykh, E. Miloslavsky, T. Nomura, R. Neff, and A. Zakhor, “Video compression using matching pursuits,” IEEE Trans. Circuits Syst. Video Technol., vol. 9, no. 1, pp. 123-143, Feb. 1999.
[2] J. L. Lin, W. L. Hwang, and S. C. Pei, “SNR scalability based on bitplane coding of matching pursuit atoms at low bit rates: fine-grained and two layers,” IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 1, pp. 3-14, Jan. 2005.
[3] D. Taubman and M. Marchelkin, JPEG2000: Image Compression Fundamentals, Standards and Practice.
[4] D. Taubman, “High performance scalable image compression with EBCOT,” IEEE Trans. Image Process., vol. 9, no. 7, pp. 1158-1170, Jul. 2000.
[5] G. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process., vol. 41, no. 12, pp. 3397-3415, Dec. 1993.
[6] R. Neff and A. Zakhor, “Very low bit rate video coding based on matching pursuits,” IEEE Trans. Circuits Syst. Video Technol., vol. 7, no. 2, pp. 158-171, Feb. 1997.
[7] ISO/IEC 15 444-1: Information Technology-JPEG 2000 Image Coding System-Part 1: Core Coding System, 2000
[8] ISO/IEC 15 444-2: Information Technology-JPEG 2000 Image Coding System-Part 2: Extension, 2000
[9] C. Christopoulos, A. Skodras, and T. Ebrahimi, “The JPEG2000 still image coding system: an overview,” IEEE Trans. Consum. Electron., vol. 46, no. 4, pp. 1103-1127, Nov. 2000.
[10] J. L. Lin, W. L. Hwang, and S. C. Pei, “Video compression based on orthonormal matching pursuits,” ISCAS.2006.
[11] J. L. Lin, W. L. Hwang, and S. C. Pei, “Multiple blocks matching pursuit update for low bit rate video coding,” IEEE Trans. Circuits Syst. Video Technol., vol. 16, no. 3, pp. 331-337, Mar. 2006.