A Review: Various Wavelet Based Image

ABSTRACT Image compression is one of the most visible applications of wavelets. A Wavelet is a finite energy signal defined over specific interval of time. Wavelets can be combined, using a "reverse, shift, multiply and sum" technique called convolution. Wavelets often give a better signal representation using Multiresolution analysis, with balanced resolu-tion at any time and frequency. Wavelets are localized in both time and frequency whereas the standard Fourier transform is only localized in frequency. Here in this paper we examined and compared various wavelet based techniques such as continuous wavelet Transform, Discrete Wavelet Transform, Wavelet Packets and Fast Wavelet transform. Wavelet packet method is a generalization of wavelet decomposition that offers a richer signal analysis. The Discrete Wavelet Transform analyzes the signal at different frequency bands with different resolutions by decomposing the signal into an approximation and detail information. Image coded by DWT do not have the problem of blocking artifacts which the DCT approach may suffer.Mallet Algorithm based fast wavelet analysis makes the use of extension of a given finite-length signal and removes the border effects due to convolution