**6. References**


**Part 5** 

**Applications in Engineering** 


**Part 5** 

**Applications in Engineering** 

10 Will-be-set-by-IN-TECH

536 Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

Calderbank, A. R., Daubechies, I., Sweldens, W. & Yeo, B. (1998). Wavelet transforms that map integer to integers, *Applied and Computational Harmonics Analysis* 5(3): 332–369. Comaniciu, D. & Meer, P. (2002). Mean shift: a robust approach toward feature space analysis, *IEEE Transactions on Pattern Analysis and Machine Intelligence* 24(5): 603–619.

Daubechies, I. & Sweldens, W. (1998). Factoring wavelet and subband transforms into lifting

Fowler, J. E. & Rucker, J. T. (2007). 3d wavelet-based compression of hyperspectral imagery, *in*

Huang, B. & Sriraja, Y. (2006). Lossless compression of hyperspectral imagery via lookup

Kulkarni, P., Bilgin, A., Marcellin, M., Dagher, J., Kasner, J., Flohr, T. & Rountree, J. (2006).

Mielikainen, J. (2006). Lossless compression of hyperspectral images using lookup tables,

Mielikainen, J. & Toivanen, P. (2003). Clustered DPCM for the lossless compression of hyperspectral images, *IEEE Trans. Geos. Remote Sensing* 41(12): 2943–2946. Mielikainen, J. & Toivanen, P. (2008). Lossless compression of hyperspectral images using a quantized index to look-up tables, *IEEE Geos. Remote Sensing Letters* 5(3). Rizzo, F., Carpentieri, B., Motta, G. & Storer, J. A. (2005). Low complexity lossless

Slyz, M. & Zhang, L. (2005). A block-based inter-band lossless hyperspectral image

Taubman, D. & Marcellin, M. (2002). *JPEG2000: Image compression fundamentals, standards and*

Wang, H., Babacan, D. & Sayood, K. (2005). Lossless hyperspectral image compression using context-based conditional averages, *IEEE Proc. Data Compression Conference* .

C.-I. Chang (ed.), *Hyperspectral Data Exploitation: Theory and Applications*, John Wiley

Compression of earth science data with jpeg2000, *in* G. Motta, F. Rizzo & J. A. Storer

compression of hyperspectral imagery via linear prediction, *IEEE Signal Processing*

Daubechies, I. (1992). *Ten lectures on wavelets*, Soc. Indus. Appl. Math.

Mallat, S. (1999). *A Wavelet Tour of Signal Processing*, Academic Press.

compressor, *IEEE Proc. Data Compression Conf.* .

*practice*, Kluwer Academic Publishers, Boston.

tables with predictor selection, *SPIE* 6365.

& Sons, Inc., pp. 379–407.

*IEEE SPL* 13(3): 157–160.

*Letters* 12(2): 138–141.

steps, *The Journal of Fourier Analysis and Applications* 4: 247–269.

(eds), *Hyperspectral Data Compression*, Springer US, pp. 347–378.

**24** 

Ching-Yu Yang

*Taiwan* 

**Robust Lossless Data Hiding by** 

*Department of Computer Science and Information Engineering* 

*National Penghu University of Science and Technology* 

**Feature-Based Bit Embedding Algorithm** 

Recently, data hiding, or information hiding, plays an important role in data assurance. Generally speaking, data hiding techniques can be classified into steganography and digital watermarking (Cox et al., 2008; Shih, 2008). The marked images generated by the steganographic methods (Gu & Gao, 2009; Liu & Shih, 2008; Qu et al., 2010; Wang et al., 2010; Zhou et al., 2010; Fan et al., 2011) were prone to catch damage (by manipulations) and resulted in a failure extraction of the message. However, based on the spatial domain, the steganographic methods often provide a large payload with a good perceived quality. Major applications of the techniques can be found in private data saving, image tagging and authentication, and covert communications. On the other hand, the robustness performance with a limited payload is a key feature of digital watermarking approaches (Lai et al., 2009; Al-Qaheri et al., 2010; Lin & Shiu, 2010; Yamamoto & Iwakiri, 2010; Yang et al., 2010; Martinez-Noriega et al., 2011). Most of the robust watermarking approaches which based on the transform domain such as discrete cosine transform (DCT), integer wavelet transform (IWT), and discrete Fourier transform (DFT) can be tolerant of common image processing operations. Their usages can be found in owner identification, proof of ownership, and copy control. Note that conventional data hiding techniques were irreversible, namely, the host media can not be recovered after data extraction. To preserve or protect the originality of the valuable (or priceless) host media, for example, military or medical images, and law enforcement, the reversible data hiding schemes, also known as lossless data hiding schemes were suggested to achieve the goal. For some applications, it requires to completely recover the host media if the marked images remain intact, and to extract the hidden message when the marked images were intentionally (or unintentionally) manipulated by the third parties. But, most of reversible data hiding schemes (Tian, 2003; Alattar, 2004; Hsio et al., 2009; Hu et al., 2009; Tai et al., 2009; Wu et al., 2009; Lee et al., 2010; Xiao & Shih, 2010; Yang & Tsai, 2010; Yang et al., 2010, 2011) were fragile in the sense that the hidden message can be unsuccessfully extract even if a slight alteration to the marked images, not to mention the recovery of the host media. Several authors (Zou et al., 2006; Ni et al., 2008; Zeng et al., 2010) therefore proposed robust reversible data hiding algorithms to overcome the issue.

Zou et al. (Zou et al., 2006) presented a semi-fragile lossless watermarking scheme based on integer wavelet transform (IWT). To obtain a good perceptual quality, they only embed data

**1. Introduction** 
