跳到主要內容

臺灣博碩士論文加值系統

(3.236.68.118) 您好!臺灣時間:2021/07/31 19:23
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:蔡信男
研究生(外文):Hsin-Nan Tsai
論文名稱:基於區塊歸類之無失真影像壓縮法
論文名稱(外文):Lossless Image Compression Method Based on Block Grouping
指導教授:陳恆佑陳恆佑引用關係張克寧
指導教授(外文):Herng-Yow ChenKeh-Ning Chang
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:29
中文關鍵詞:無失真影像壓縮區塊歸類
外文關鍵詞:Lossless Image Compression MethodBlock Grouping
相關次數:
  • 被引用被引用:0
  • 點閱點閱:131
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本論文提出一無失真灰階影像壓縮技術。此方法先以中間邊緣偵測法(Median Edge Detector, MED)產生一差值影像(Difference image)與一符號影像(Sign image),為了降低符號影像之複雜度,本論文採用位元轉移法(bits transformation)將符號影像與差值影像之最重要位元(MSB)進行交換,使此符號影像之複雜度能得以降低。接著以區塊歸類法(Blcok classification)將影像區塊歸類至資料容器(Data bin),每一個資料容器皆包含不同的資料樣本。最後,將各個資料容器的資料用算術編碼法(Arithmetic coding)壓縮。

實驗結果顯示本論文所提出之方法在影像複雜度較低的情形下,所得結果比JPEG 2000之無失真壓縮法好。同時此實驗結果也比其它無失真壓縮法如GIF、PNG、TIFF來得好。
This thesis proposes a lossless image compression technique for gray level images. This method employs a median edge detector (MED) on the input image to produce a difference image and a sign image. Then, in order to reduce the complexity of the sign image, bits transformation is adopted to transform the bits between the sign image and the most significant bits (MSB) in difference image. The resultant image blocks, according to the maximum value of image block, are classified into data bins, each of which has different data patterns. Finally, the arithmetic coding method is applied to compress the data in each data bin respectively.

The result of this thesis shows that the proposed method when applied in smooth images provides a better bit rate than JPEG 2000 lossless method does. And the result is also better than other lossless compression methods such as GIF, PNG, and TIFF.
Table of Content I
List of Figures II
List of Tables III
Chapter 1: Introduction 1
1.1 Motivation and Goal 2
1.2 Organization of this Thesis 2
Chapter 2: Related Works 3
2.1 Lossless image compression technology 3
2.2 Lossy image compression technology 5
2.3 Image evaluation 7
Chapter 3: Lossless Image Compression Method Based on Block Grouping 9
3.1 MED processing 10
3.2 Bits transformation 17
3.3 Block classification 19
3.4 Arithmetic coding 19
3.5 Image decompression 20
Chapter 4: Experimental results 22
Chapter 5: Conclusion 26
Reference 27
[1] Diego, S. C., Raphael, G., and Touradj, E., “JPEG 2000 Performance Evaluation and Assessment,” Signal Processing: Image Communication, Vol. 17, Issue: 1, January, 2002, pp. 113-130.
[2] ISO/IEC FCD 155444-1, Information Technology-JPEG 2000 Image Coding System, 2000.
[3] Skodras, A. N., Christopoulos, C. A., Ebrahimi, T., “JPEG 2000: The upcoming still image compression standard,” Pattern Recognition Letters, pp.1337-1345, 2001.
[4] Weinberfger, M., Serousai, G., Sapiro, G., “The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS,” Hewlett-Packard Laboratories Technical Report, No HPL-98-193, 1998.
[5] Chen, B., Latifi, S., and Kanai, J., “Edge enhancement of remote image data in the DCT domain,” Image and Vision Computing, Vol. 17, No. 12, pp.913-921, October, 1999.
[6] Fang, W. H., Hu, N. C. and Shih, S. K., “Recursive fast computation of the two-dimensional discrete cosine transform,” IEEE Proceedings on Vision, Image and Signal Processing, Vol. 146, No. 1, pp.25-33, Feb. 1999.
[7] Shapiro, J. M., “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Transactions on Signal Processing, Vol. 41, No. 12, pp.3445-3462, 1993.
[8] Bei, C. D. & Gray, R. M. (1985), “An improvement of the Minimum Distortion Encoding Algorithm for Vector Quantization,” IEEE Trans. on Comm., vol. 33, pp. 1132-1133.
[9] Buzo, A. et al. (1980), “Speech coding based upon vector quantization,” IEEE Trans. Acoust., Speech, Singal Processing, vol. ASSP-28, pp. 562-574.
[10] Linde, Y., Buzo, A., and Gray, R. M., “An algorithm for vector quantizer design,” IEEE Trans. on Communications, Vol.28, No.1, 1980, pp.84-95.
[11] Antonini, M., Barlaud, M., Mathieu, P. and Daubechies, I. “Image coding using wavelet transform,” IEEE Trans. Image Processing, Vol. 1, pp.205-220, 1992.
[12] Craizer, M., Silva, E. A. B. D. and Ramos, E. G., “Convergent algorithms fpr successive approximation vcetor quantization with applications to wavelet image compression,” IEE Proceedings-Vision Image and Signal Processing, VOl. 146, No. 3, pp.159-164, Jun. 1999.
[13] Jarvi, A., Lehtien, J. and Nevalainen, O., “Variable quality image compression system based on SPIHT,” Signal Processing: Image Communication, Vol. 14, No. 9, pp.683-696, 1999.
[14] Linde, Y., Buzo, A., and Gray, R. M., “An algorithm for vector quantizer design,” IEEE Transactions on Communications, Vol. 28, No. 1, pp.84-95, 1980.
[15] Losada, M. A., Tohumoglu, G., Fraile, D. and Artes, A., “Multi-iteration wavelete zero-tree coding for image compression,” Signal Processing, pp.1281-1287, 2000.
[16] Wu, B. F. and Su, C. Y., “Low computational complexity enhanced zerotree coding for wavelet-based image compression,” Signal Processing: Image communication, pp.401-411, 2000.
[17] Utku, C. M., Gaurav, S., and Murat, T. A., “Gray-Level-Embedded Lossless Image Compression,” Signal Processing: Image Communication, Vol. 18, Issue: 6, pp.443-454, July, 2003.
[18] Hu, Y. C. and Chang, C. C., “A New Lossless Compression Scheme Based on Huffman Coding Scheme for Image Compression,” Signal Processing: Image Communication, Vol. 16, Issue: 4, pp.367-372, November, 2000.
[19] Huffman, D. A., “A Method for the Construction of Minimum Redundancy codes,” Pro. IRE, 40, pp.1098-1101, 1952.
[20] Knuth, D. E., “Dynamic Huffman coding,” J. Algorithms, 6, pp.163-180, 1985.
[21] Langdon, G. G., “An Introduction to Arithmetic Coding,” IBM J. Res. De., 28(2), pp.135-149, 1985.
[22] Rissanen, J. and Langdon, G. G., “Arithmetic coding,” IBM J. Res. De., 23(2), pp.149-162, 1979.
[23] Terry, W., “A technique for High-Performance Data Compression,” IEEE Computer, 17(6), pp.8-19, 1984.
[24] Shannon, C. E., “Prediction and entropy of printed English,” Bell Sys. Tech. J., 30, pp.50-64, 1951.
[25] Shannon, C. E., “A mathematical Theory of Communication,” Bell Sys. Tech. J., 27, pp.379-423, pp.623-656, 1948.
[26] Martucci and Stephen, A., “Reversible compression of HDTV images using median adaptive prediction and arithmetic coding.” In IEEE International Symposium on Circuits and Systems, pp.1310-1313, New York, 1990.
[27] Shih, F. Y. and Chen, S. S., “Adaptive document block segmentation and classification,” IEEE Trans. Systems, Man, and Cybernetics, vol. 26, no. 5, pp. 797–802, 1996.
[28] 施威銘研究室,PC影像處理技術.圖檔壓縮篇,旗標出版社,台北,1993。
[29] 施威銘研究室,PC影像處理技術.圖檔壓縮續篇,旗標出版社,台北,1994。
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top