TY  - JOUR
T1  - Performance Analysis and Comparison of Wavelet Families Using for Image Compression
AU - , Yogendra Kumar Jain AU - , Sanjeev Jain 
JO  - International Journal of Soft Computing
VL  - 2
IS  - 1
SP  - 161
EP  - 171
PY  - 2007
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2007.161.171
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2007.161.171
KW  - Wavelets
KW  -wavelet families
KW  -wavelet transform
KW  -Image compression
KW  -compression performance
KW  -peak signal to noise ratio
AB  - The aim of this study is to analyze and compare a set of wavelet families using for image compression. The study discusses important features of wavelet transform in compression of images. The wavelet transform is  a  fast  developing tool for image compression and provides efficient compression performance. Especially for  high  compression  ratios, wavelets perform much better than competing technologies both in terms of signal-to-noise ratio and visual quality. Wavelet transformation provides both spatial and frequency domain information of image. Wavelet Transform (WT) decomposes an image into wavelet function (wavelets) at different resolution levels. Therefore, the wavelet transform can be composed of function that satisfies requirements of multiresolution analysis. Depending on the application, different aspects of wavelets can be emphasized. The selection of wavelet for image compression depends on the image application and image contents. A review of the wavelet families using for image compression is given here. In this study we have analyzed various wavelets of different wavelet families (such as Biorthogonal, Daubechies, Reverse Biorthogonal, Symlets and Coiflets) performing image compression on variety of test images. The test images are of different frequency content, size and resolution. We have also analyzed effects of wavelet functions belonging to each of these wavelet families at a compression ratio of 100:1 at decomposition level 5 on the variety of test images. The results of compression performance for different wavelets of different wavelet family, image contents, compression ratios and resolutions are given. The image quality is measured, objectively peak signal-to-noise ratio and subjectively visual quality of image.
ER  -