臺灣博碩士論文加值系統

在本論文的研究中，我們提出以整數小波 (integer wavelet) 和灰色理
論 (grey theory) 為基礎的漸進式影像壓縮技術。在漸進式壓縮方面，我們以小波分解技術來產生不同解析度及不同波段 (subband) 的影像。同時配合灰色理論在高頻波段上做預測以達到更高的壓縮倍率。有別於傳
統影像壓縮技術由左而右由上而下的編碼。在網路瀏覽時可能因為網路
頻寬的限制而只能瀏覽半張影像。而我們所提出的方法可以因為頻寬的
限制而看到相較原始影像二分之一解析度的影像而不是只看到半張影
像，如此在瀏覽資料量較大的影像或是頻寬壅塞時更有利於上線的使用者。

In this paper a progressive image compression approach based on
our proposed integer wavelet transform and grey prediction is proposed.
The integer wavelet transform is based on a reversible round-off linear
transform algorithm to forward and backward transform integers without
any loss. The proposed compression approach is combining the EZW
method and a grey prediction to compress images. EZW is a famous
wavelet-based image compression and the grey prediction is based on
the grey theory to further improve the compression rate without
degrading the image quality. The proposed approach is suitable for
browsing large-scaled images. Several experiments and comparisons are
conducted to evaluate the performance of the proposed approach.

摘 要I
誌 謝II
目 錄III
第一章緒論一
第二章相關研究二
第三章小波分解三
第四章編碼演算法四
第五章灰色預測五
第六章實驗與討論六
第七章結論七
附 錄英文版論文八

[1]Autonimi, M., M. Barlaud, P. Mathieu, and I. Daubechies, “Image coding using wavelet transform,” IEEE Trans. Image Processing, Vol.1, pp.205-220, 1992.
[2]Calderbank, A. R., I. Daubechies, W. Sweldens, and B. Yeo, Wavelet Transform That Map Integers to Integers, Tech. Rep., Dept. of Mathematics, Princeton University, Princeton, NJ, 1996.
[3]Cohen, A., I. Daubechies, and J. Feauveau, “Bi-orthogonal bases of compactly supported wavelets,” Commun. Pure Appl. Math., Vol.45, pp.485-560, 1992.
[4]Daubechies, I. and W. Sweldens, Factoring Wavelet Transforms into Lifting Steps, Bell Laboratories, Lucent Technologies, Murray Hill, NJ, 1996.
[5]Deng, J.-L., “Control problem of grey system ,“ Systems and Control Letters, Vol.1, No.5, pp.288-294, 1982.
[6]Deng, J.-L., “Grey system,” China’a Future and Development, No.4, pp.249-252, 1984.
[7]Deng, J.-L., “Properties of Relational Space for Grey System,“ Grey System, Beijing, China Ocean Press, pp.1-13, 1988.
[8]Deng, J.-L., “Introduction to grey systems therory, ” The Journal of Grey System, Vol.1, No.1, pp.1-24, 1989.
[9]Deng, J.-L., “Grey information Space,” The Journal of Grey System, Vol.1, No.2, pp.103-117, 1989.
[10]Dewitte, S. and J. Cornelis, “Lossless integer wavelet transform,” IEEE Signal Processing Letters, Vol.4, pp.158-160, 1997.
[11]Golchin, F. and K. Paliwal, “Quadtree based classification with arithmetic and trellis coded quantization for subband image coding,” in Proc. Int. Conf. ASSP, 1997, pp.2921-2924.
[12]Heer, V. K. and H.-E. Reinfelder, “A comparison of reversible methods for data compression,” in Proc. SPIE of Medical Imaging IV, 1990, pp.354-365.
[13]Huang, H.-C. and J. L. Wu, ”Grey system theory on imgae processing and lossless data compression for HD-media,” Journal of Grey Theory & Pratic, Vol.3, No.2, pp.9-15, 1993.
[14]Huang, Y.-P., H. C. Chu, and K. H. Hisa, “Dynamic grey modeling: theory and application,” in Proc The First National Symposium on Grey System Theory and Its Application, Taiwan, 1996, pp.47-56.
[15]Joshi, R. L., V. J. Crump, and T. R. Fischer, “Image subband coding using arithmetic coded trellis coded quantization,” IEEE Trans. Circuits Syst. Vedio Technol., Vol.6, pp.515-523, 1995.
[16]Joshi, R. L., H. Jafarkhani, J. H. Kasner, T. R. Fischer, N. Farvardin, M. W. Marcellin, and R. H. Bamberger, “Comparison of different methods of classification in subband coding of images” IEEE Trans. Image Processing, Vol.6, pp.1473-1486, 1997.
[17]Munteanu, A., J. Cornelis, G. Van der Auwera, and P. Cristea, “A wavelet based lossless compression scheme with progressive transmission capability,” Int. J. Imaging Syst. Technol., Vol.10, pp.76-85, 1999.
[18]Said, A. and W. Pearlman, “An image multiresolution representation for lossless and lossy compression,” IEEE Trans. Image Processing, vol.5, No.3, pp.1303-1310, 1996.
[19]Shapiro, J. M., “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Trans. Signal Processing, Vol.41, No.12, pp.3445-3462, 1993.
[20]Shusterman, E. and M. Feder, “Image compression via improved quadtree decomposition algorithms,” IEEE Trans. Image Processing, Vol.3, pp.207-215, 1994.
[21]Sodagar, I., H.-J. Lee, P. Hatrack, and Y.-Q. Zhang, “Scalable wavelet coding for synthetic/natural hybrid images,” IEEE Trans. Circuits Syst. Video Technol., vol. 9, no. 2, pp. 244-254, 1999.
[22]Strobach, P., “Quadtree-structured recursive plane decomposition coding of image,” IEEE Trans. Signal Processing, Vol.39, pp.1380-1397, 1991.
[23]Sullivan, E. and R. L. Baker, “Efficient quadtree coding o images and video,” IEEE Trans. Image Processing, Vol.3, 1994, pp.327-331.
[24]Sweldens, W., “The lifting scheme: A custom design construction of biorthogonal wavelets,” J. Appl. Comput. Harmonic Anal., Vol.3, pp.186-200, 1996.
[25]Teng, C.-Y., D. L. Neuhoff, J. A. Storer, and M. Cohn, “Quadtree-duided wavelet image coding,” in Proc. IEEE Data Compression Conf., 1996, pp.406-415.
[26]Wang, H.-J. and C.-C. Kuo, “A multi-threshold wavelet coder (MTWC) for high fidelity image compression,” IEEE Trans. Image Processing, Vol.1, pp.652-655, 1997.
[27]Woods, J. W. and S. D. O’Neil, “Subband coding of images,” IEEE Trans. Acoust., Speech, Signal Processing, Vol.34, pp. 1278-1288, 1986.
[28]Wu, X., “Lossless compression of continuous-tone images via context selection, quantization, and modeling,” IEEE Trans. Image Processing, Vol.6, pp.656-664, 1997.
[29]Zandi, A., J. D. Aleen, E. L. Scwartz, and M. Boliek, CREW: Compression with Reversible Embedded Wavelet, Tech. Rep. CRC-TR-9526, RICOH California Research Center, 1995.
[30]Zhang, Y.-Q. and S. Zafar, “Motion-compensated wavelet transform coding for color compression,” IEEE Trans. Circuits Syst. Video Technol., Vol. 2, pp. 285-296, 1992.