Systolic architecture for discrete wavelet transforms with orthonormal bases

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Abstract
The wavelet transform provides a new method for signal/image analysis where high frequency components are studied with finer time resolution and low frequency components with coarser time resolution. It decomposes a scanned signal into localized contributions for multiscale analysis. This paper presents a general systolic architecture for efficient computations of both signal decomposition and signal reconstruction with orthonormal wavelet bases. When the number of data points windowed in the input is W equals 2m, our discrete wavelet transform (DWT) systolic architecture is composed of m layers of 1-dimensional arrays, which compute the high-pass and the low-pass filtered components simultaneously. Input data string can enter and be processed on-the-fly continuously at the rate of one data point per clock period T. For an input signal of length N (multiple of W), the computation time is NT when N is large. Multiple DWT problems can be pipelined through the array. The computation time for a large number of successive DWT problems is also NT per DWT.

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