Image compression using wavelets

The discrete wavelet transform (DWT) represents images as a sum of wavelet functions (wavelets) on different resolution levels. The basis for the wavelet transform can be composed of any function that satisfies requirements of multiresolution analysis. It means that there exists a large selection of wavelet families depending on the choice of wavelet function. The choice of wavelet family depends on the application. In image compression application this choice depends on image content. This paper provides fundamentals of wavelet based image compression. The options for wavelet image representations are tested. The results of image quality measurements for different wavelet functions, image contents, compression ratios and resolutions are given.

[1]  V. Ralph Algazi,et al.  Comparative study of wavelet image coders , 1996 .

[2]  Barbara Hubbard,et al.  The World According to Wavelets , 1996 .

[3]  William R. Zettler,et al.  Application of compactly supported wavelets to image compression , 1990, Other Conferences.

[4]  Martin Vetterli,et al.  Spatially adaptive wavelet thresholding with context modeling for image denoising , 2000, IEEE Trans. Image Process..

[5]  James S. Walker,et al.  A Primer on Wavelets and Their Scientific Applications , 1999 .

[6]  C. Mulcahy,et al.  Image Compression Using Haar Wavelet Transform , 2011 .

[7]  Hong-Ye Gao,et al.  Applied wavelet analysis with S-plus , 1996 .

[8]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[9]  Martin Vetterli,et al.  Image denoising via lossy compression and wavelet thresholding , 1997, Proceedings of International Conference on Image Processing.

[10]  H. L. Resnikoff,et al.  Wavelet analysis: the scalable structure of information , 1998 .

[11]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1991, CACM.

[12]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[13]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[14]  Jerome M. Shapiro,et al.  Embedded image coding using zerotrees of wavelet coefficients , 1993, IEEE Trans. Signal Process..

[15]  Pamela C. Cosman,et al.  Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy , 1994, Proc. IEEE.

[16]  I. Stewart.  LITTLE WAVES, BIG SQUEEZE , 1996 .

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

[18]  William A. Pearlman,et al.  A new, fast, and efficient image codec based on set partitioning in hierarchical trees , 1996, IEEE Trans. Circuits Syst. Video Technol..

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