DWT (Discreet Wavelet Transform)

The discrete wavelet transform is a valuable way designed for signal exploration as well as picture handling, chiefly in multi-resolution description. Wavelets are described as the functions obtained over a fixed interval and have zero as an average value. This transformation is an extremely necessary way to be used for signal investigation as well as image processing, mainly for multi-resolution demonstration. It may crumble signal into different components in the frequency sphere [10]. One-dimensional discrete wavelet transform (1-D DWT) decomposes an input into two components (the average component and the detail component).

The 2-dimensional (2-D) DWT distributes an input picture into four type of sub-bands, single average component (LL) and three detail components (LH, HL, HH) as shown in Figure [6].

Advantage of DWT over DCT

• No need to divide the input coding into non-overlapping 2-D blocks, it has higher compression ratios avoid blocking artifacts.
• Allows good localization both in time and spatial frequency domain.
• Transformation of the whole image introduces inherent scaling.
• Better identification of which data is relevant to human perceptionhigher compression ratio.
• Higher flexibility: Wavelet function can be freely chosen.