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研究生:李健豪
研究生(外文):Chien-Hao Li
論文名稱:不均等錯誤保護技術於SLCCA影像壓縮之研究
論文名稱(外文):Unequal Error Protection on SLCCA Image Encoded Bit Stream
指導教授:謝文雄謝文雄引用關係
指導教授(外文):Wen-Shyong Hsieh
學位類別:碩士
校院名稱:國立中山大學
系所名稱:資訊工程學系研究所
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:2002
畢業學年度:90
語文別:英文
論文頁數:41
中文關鍵詞:不均等錯誤保護
外文關鍵詞:SLCCAUnequal Error Protection
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這篇論文中,我們提出一個不均等錯誤保護技術在SLCCA影像壓縮技術上。
由於SLCCA所產生的重要係數地圖和量的重要性交錯排列,如果我們施予相同的保護,會對不重要的資料有過多的保護。
我們提出一個重組的方法,利用SLCCA的重要係數地圖和量的重要性,由重要排序至不重要。然後再對重要的資料給予較多的保護,相對地,不重要的資料給予較少的保護,以避免對不重要有過多的保護,及有較好的錯誤修復效能。


In SLCCA , the location and magnitude of significant coefficients are specified by the so-called significance map and magnitude respectively . As we know significance map is susceptible , error will propagate when data was deteriorated .
This paper address this critical problem and provide an novel approach . In the significance map , the importance of data is interlaced . And our approach is to re-organize the significant map according to encoded symbol’s characteristic . In SLCCA , four symbols are used to encode : POS , NEG , ZERO , LINK . POS or NEG represents the sign of a significant coefficient . ZERO represents an insignificant coefficient . LINK marks the presence of a significance-link . Symbol LINK is more important than POS NEG ZERO . Because when error happen in symbol LINK , it will lead to propagation error . Re-organized data is protected by differRS code . More important data are allocated more parity symbols .


Contents
1 Introduction……………………………………………………………1
2 Abstract of SLCCA……………………………………………………4
2.1 Discrete Wavelet Transform……………………………………4
2.1.1 Abstract of Discrete Wavelet Transform…………………4
2.1.2 Scan Order of Discrete Wavelet Transformed Coefficient
(in SLCCA )………………………………………………5
2.2 Quantization……………………………………………………7
2.3 Connected Components………………………………………10
2.4 Significance-Link………………………………………………16
2.5 Adaptive Arithmetic Coding……………………………………18
2.6 SLCCA Algorithm……………………………………………21
3 Related Word…………………………………………………………24
4 Proposed method……………………………………………………31
4.1 Concept…………………………………………………………31
4.2 Implement Issue………………………………………………32
5 Simulation……………………………………………………………36
6 Conclusion…………………………………………………………39
List of Figures
Fig 2.1.2.1 the order of SLCCA transmit subband……………………6
Fig 2.1.2.2 the order of SLCCA scan coefficients within subband……7
Fig2.2.1 result which passed through discrete wavelet transform …8
Fig2.2.2 result of quantization ..……………………………………9
Fig2.3.1 SLCCA search order..……………………………………12
Fig 2.3.2(a) SLCCA search order……………………………………….12
Fig 2.3.2(b) SLCCA search order……………...…………………….…12
Fig 2.3.3 formation connected component example………………13
Fig 2.3.4 picture Lena after discrete wavelet transforming………15
Fig 2.4.1 the relation of significant link…………………………16
Fig 2.4.2 a example of significant link ……...………………………17
Fig 3.1 Bit stream demultiplexing………………………………25
Fig 3.2 re-organized bit stream……………………………………26
Fig 3.3 Error resilient source bit stream packetization……………28
Fig 4.1.1 3-scale wavelet image…...………………………………32
Fig 4.2.1 SLCCA significant map bit-stream……………………34
Fig 4.2.2 re-organized SLCCA’s significant map…………………34
Fig 4.2.3 divide Significant magnitude bit-stream to two part……35
Fig5.1 Performance comparison of the proposed codec for the “airplane” image at 1bpp………………………………37
Fig5.2 Performance comparison of the proposed codec for the “toys” image at 1bpp……………………………………37
Fig5.3 Performance comparison of the proposed codec for the “Lena” image at 1bpp……………………………………38
Fig5.4 Performance comparison of the proposed codec for the “pepper” image at 1bpp…………………………………38


Reference
[1] J. M. Shapiro , “Embedded image coding using zerotrees of wavelet
coefficients,“ IEEE Trans. Signal Processing, vol. 41, pp. 3445-3462, Dec. 1993.
[2] S. Servetto , k. Ramchandran , and M. T. Orchard, “Wavelet based
image coding via morphological predication of significance,” in Proc. IEEE Int. Conf. Image Processing, OCT. 1995, pp. 530-533.
[3] A. Said and W. A. Pearlman, “A new , fast , and efficient image codec
based on set partitioning in hierarchical trees,” IEEE Trans. Circuits Syst. Video Technol. , vol. 6, pp. 243-250, June 1996.
[4] B. B. Chai, J. Vass , X. Zhuang. “Significance-linked connected
component analysis for wavelet image coding ,“. IEEE Transctions on Image Processing , 8(6):774-784,June 1999.
[5] M. Antonini , M. Barlaud , P. Mathieu , and I. Daubechies , “ Image
coding using wavelet transform ,” IEEE Trans. Image Processing , vol. 1 , pp.205-220 , Apr. 1992.
[6] G. G. Langdon, “An Introduction to Arithmetic Coding,” IBM J Res. Develop., Vol.28, no 2, pp. 135-149,Mar 1984
[7] I. H. Witten , R. M. Neal and J. G. Cleary , “Arithmetic Coding for Data Compression,” Communication of the ACM , Vol. 30, No. 6, pp. 520-540 , June 1987
[8] A. Aydim Alatan, Minyi Zhao, and Ali N. Akansu, “Unequeal Error Protecion of SPIHT Encoded Image Bit Streams”, IEEE Journal on Selected Areas in Communications, Vol. 18, No. 6, June 2000
[9] T. Collins and P. Atkins , “Error-tolerant SPIHT image compression”, IEEE Proc..-Vis. Image Signal Process, Vol. 148, No. 3 June 2001.
[10] J. Vass , X. Zhuang ,”Enhanced Significance-Linked Connected Component Analysis for High Performance Error Resilient Wavelet Image Coding ,” IEEE Proc. , Vol. 3 , 2000

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