A Family of Multiplier Free Orthogonal Wavelet Filter Banks

This chapter presents a generalized novel design of a family of multiplier free orthogonal wavelet filter banks (FBs) with low complexity. In this, maximum number of zeros at \(\mathrm {z} = -1\) is ensured for a desired length of analysis and synthesis filters. A new class of technique is suggested to obtain dyadic filter coefficients based on double shifting orthogonality property with allowable deviation from original filter coefficients. This is achieved by slightly altering perfect reconstruction (PR) condition to obtain dyadic filter coefficient. A general closed form expression is formulated using double shifting orthogonality property to reduce the error between original filter coefficients and dyadic filter coefficients. The designed wavelet FB improved time-frequency product, Sobolev regularity and frequency selectivity (low energy in error) over well-known existing wavelet FBs. The performance of the suggested wavelet FB is validated on two different applications namely image compression and medical image retrieval. The suggested FBs are tested on well-known datasets such as CLASSIC, EPFL, RAISE, and FiveK for image compression and on three different medical image databases such as NEMA, OASIS, and EXACT09 for image retrieval.