Color Image Compression Based On Wavelet Packet Best Tree

In Image Compression, the researchers’ aim is to reduce the number of bits required to represent an image by removing the spatial and spectral redundancies. Recently discrete wavelet transform and wavelet packet has emerged as popular techniques for image compression. The wavelet transform is one of the major processing components of image compression. The result of the compression changes as per the basis and tap of the wavelet used. It is proposed that proper selection of mother wavelet on the basis of nature of images, improve the quality as well as compression ratio remarkably. We suggest the novel technique, which is based on wavelet packet best tree based on Threshold Entropy with enhanced run-length encoding. This method reduces the time complexity of wavelet packets decomposition as complete tree is not decomposed. Our algorithm selects the sub-bands, which include significant information based on threshold entropy. The enhanced run length encoding technique is suggested provides better results than RLE. The result when compared with JPEG-2000 proves to be better.

[1]  Ronald R. Coifman,et al.  Fast wavelet packet image compression , 1998, Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225).

[2]  Mark Nelson,et al.  The Data Compression Book , 2009 .

[3]  Andreas Uhl,et al.  Wavelet Packet Best Basis Selection on Moderate Parallel MIMD Architectures , 1996, Parallel Comput..

[4]  Casimer M. DeCusatis,et al.  Wavelets and Subbands: Fundamentals and Applications , 2002 .

[5]  Sethuraman Panchanathan,et al.  Choice of Wavelets for Image Compression , 1995, Information Theory and Applications.

[6]  D. Gupta,et al.  Image compression using wavelet packets , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

[7]  Andrew B. Watson,et al.  Image Compression Using the Discrete Cosine Transform , 1994 .

[8]  Manuel P. Malumbres,et al.  Fast and efficient spatial scalable image compression using wavelet lower trees , 2003, Data Compression Conference, 2003. Proceedings. DCC 2003.

[9]  Subhasis Saha,et al.  Image compression—from DCT to wavelets: a review , 2000, CROS.

[10]  Michael T. Orchard,et al.  Image coding based on a morphological representation of wavelet data , 1999, IEEE Trans. Image Process..

[11]  S. Mishra,et al.  Image Compression Using Wavelet Packet Tree , 2010 .

[12]  Norman D. Black,et al.  Second-generation image coding: an overview , 1997, CSUR.

[13]  Francoise Pelle ISSN , 2002 .