Efficient array architectures for multi-dimensional lifting-based discrete wavelet transforms

Efficient array architectures for multi-dimensional (m  -D) discrete wavelet transform (DWT), e.g. m=2,3m=2,3, are presented, in which the lifting scheme of DWT is used to reduce efficiently hardware complexity. The parallelism of 2m2m subbands transforms in lifting-based mm-D DWT is explored, which increases efficiently the throughput rate of separable mm-D DWT with fewer additional hardware overhead. The proposed architecture is composed of m2m-1m2m-1 1-D DWT modules working in parallel and pipelined, which is designed to process 2m2m input samples per clock cycle, and generate 2m2m subbands coefficients synchronously. The total time of achieving one   level of decomposition for a 2-D image of size N2N2 is approximately N2/4N2/4 intra-clock cycles (ccs  ), and that for a 3-D image sequence of size MN2MN2 is approximately MN2/8ccs. Efficient line-based architecture frameworks for both 2D+t (spatial domain decomposition first, followed by temporal directional decomposition) and t+2D (temporal directional decomposition first, followed by spatial domain decomposition) 3-D DWT are firstly proposed, as much as we know. Compared with the similar works reported in previous literature, the proposed architectures have good performance in terms of throughput rate and system output latency, and are good alternatives in tradeoff between throughput rate and hardware complexity. The proposed architectures are simple, regular, scalable and well suited for VLSI implementation.