基於小波轉換之低複雜度影像編解碼演算法

A Low Complexity Image Coding Algorithm Based on Wavelet Transform

洪素禎(Su-Zhen Hong)

指導教授 ： 李宗演

國立臺北科技大學/電資學院/電力電子產業研發碩士專班/碩士(2007年)

https://doi.org/10.6841/NTUT.2007.00241

摘要

現今以小波轉換為基礎的影像壓縮技術已有許多的應用，例如JPEG2000及MPEG-4皆使用小波轉換做為影像壓縮的技術，所以在小波係數上的編碼演算法即成為一個基本及重要的研究。本論文提出一個反向掃描與最低樹編碼(Backward Scan and Lowest Tree Coding, BSLTC)演算法，主要是利用小波轉換後所產生的小波係數特性來進行編碼的動作，利用反向掃描來加速執行時間，利用最低樹編碼來節省記憶體使用量及縮短編碼時間，因此得到的影像壓縮的效能是比早期所提出的SPIHT及最近提出的LTW演算法與最新靜態影像規格JPEG2000所使用的一個軟體JasPer之效能還要好，而演算法的時間複雜度也較SPIHT小，並且從實驗結果可得知，在編碼執行時間上大約可比SPIHT平均快33毫秒(ms)，比JasPer/JPEG2000平均快173毫秒(ms)，且比LTW平均快3毫秒(ms)，所使用記憶體使用大小比SPIHT少102 kBytes，並且比LTW少26 kBytes。

關鍵字

影像壓縮 ； 離散小波轉換 ； 漸進式編碼 ； 架構樹集合分割壓縮編碼

並列摘要

Recently, many image compression applications are based on wavelet transform, such as static JPEG2000 and dynamic MPEG-4. Therefore, the wavelet coefficient encoding algorithm is more basic and important research. In this work, we propose a BSLTC (Backward Scan and Lowest Tree Coding) algorithm based on wavelet transform to compress image signal. The BSLTC algorithm provides the advantages of low complexity, reduced memory usage and coding time. The BSLTC algorithm has lower time complexity than SPIHT. Experiment results show that the BSLTC can gains faster execution time and less memory usage. Compared with SPIHT, JasPer/JPEG2000 and LTW, our proposed BSLTC speed up the average of 33.023 ms, 173.338 ms, and 3.532 ms on execution time, respectively. Compared with SPIHT and LTW, our proposed BSLTC reduces 102 kBytes and 26 kBytes on memory usage, respectively.

並列關鍵字

Image Compression ； Discrete Wavelet Transform (DWT) ； Progressive Coding ； Set Partitioning in Hierarchical Trees (SPIHT)

參考文獻

[1] N. Ahmed, T. Natarajan and K. R. Rao, “Discrete cosine transform,” IEEE Trans. in Computers., vol. C-23, no. 1, pp. 90-93, Jan. 1974.

[2] J. M. Shaprio, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Trans. on Signal Processing, vol. 41, pp. 3445-3459, Dec. 1993.

[3] A. Said and W. A. Pearlman, “A new fast and efficient image codec based on set partitioning in hierarchical trees,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 6, no. 3, pp. 243-250, 1996.

[4] D. Taubman, “High performance scalable image compression with EBCOT,” IEEE Trans. on Image Processing, vol. 9, pp. 1158-1170, July 2000.

[6] K. Ramchandran, M. Vetterli and C. Herley, “Wavelets, subband coding, and best bases,” in Proceedings of the IEEE, vol. 84, no. 4, pp.541-560, April 1996.