I. Introduction
The discrete wavelet transform (DWT) has the capability to analyze real-time data and signal concurrently in both time and frequency domain [1]. Due to this characteristics, DWT is widely used in applications such as pattern recognition, bio-metrics, data mining etc. [2]. Orthogonal wavelet FBs are preferred over bi-orthogonal wavelets due to their better energy preserving property and computational superiority [3]. Daubechies wavelets (Daub-1-Daub-10) are a popular wavelet family to construct the compact support orthogonal wavelets [4]. It is found that higher order Daubechies wavelets give increased wavelet regularity, better frequency localization and higher transform coding gain as compared to lower order wavelets [4]. Also, higher order FB such as Daub-4 has advantages over Daub-2 and Daub-3 in terms of reconstruction accuracy and performance.