VLSI Architectures of Lifting-Based Discrete Wavelet Transform

Abstract

The advantages of the wavelet transform over conventional transforms, such as the Fourier transform, are now well recognized. Because of its excellent locality in time-frequency domain, wavelet transform is remarkable and extensively used for signal analysis, compressing and denoising. Defining DWT by Mallat [1] provided possibility of its digitally hardware or software implementation. The discrete wavelet transform (DWT) performs a multiresolution signal analysis which has adjustable locality in both the space (time) and frequency domains [1]. Unlike the Fourier transform, the wavelet transform has many possible sets of basis functions. A trade-off can be made between the choice of basis functions and the complexity of the corresponding hardware implementations. Using finite impulse response (FIR) filters and then subsampling is the classical method for implementing the DWT. Due to the large amount of computations required, there have been many research efforts to develop new rapid algorithms [2]. In 1996, Sweldens presented a lifting scheme for a fast DWT, which can be easily implemented by hardware due to significantly reduced computations [3]. This method is entirely based on a spatial interpretation of the wavelet transform. Moreover, it provides the capability of producing new mother wavelets for the wavelet transform, based on space domain features. Due to recent advances in the technology, implementation of the DWT on field programmable gate array (FPGA) and digital signal processing (DSP) chips has been widely developed. As described in Sect. 3, in the lifting scheme the structural processing elements, including multipliers, are arranged serially; hence, the number of multipliers in each pipeline stage determines the clock speed of the structure. Based on [4], the main challenges in the hardware architectures for 1-D DWT are the processing speed and the number of multipliers, while for 2-D DWT it is the memory issue that dominates the hardware cost and the architectural complexity. The reason is the limitation of the on-chip memory and the power consumption [4,5].