Efficient Architecture for Adaptive Directional LiftingBased Wavelet Transform Adaptive direction liftingbased wavelet transform (ADL) has better performance than conventional lifting both in image compression and denoising. However, no architecture has been proposed to hardware implement it because of its high computational complexity and huge internal memory requirements. In this paper, we propose a fourstage pipelined architecture for 2 Dimensional (2D) ADL with fast computation and high data throughput. The proposed architecture comprises column direction estimation, column lifting, row direction estimation and row lifting which are performed in parallel in a pipeline mode. Since the column processed data is transposed, the row processor can reuse the column processor which can decrease the design complexity. In the lifting step, predict and update are also performed in parallel. For an 8xc3x978 image subblock, the proposed architecture can finish the ADL forward transform within 78 clock cycles. The architecture is implemented on Xilinx Virtex5 device on which the frequency can achieve 367 MHz. The processed time is 212.5 ns, which can meet the request of realtime system.

A comparative study of image compression based on directional wavelets Discrete wavelet transform is an effective tool to generate scalable stream, but it cannot efficiently represent edges which are not aligned in horizontal or vertical directions, while natural images often contain rich edges and textures of this kind. Hence, recently, intensive research has been focused particularly on the directional wavelets which can effectively represent directional attributes of images. Specifically, there are two categories of directional wavelets: redundant wavelets (RW) and adaptive directional wavelets (ADW). One representative redundant wavelet is the dualtree discrete wavelet transform (DDWT), while adaptive directional wavelets can be further categorized into two types: with or without side information. In this paper, we briefly introduce directional wavelets and compare their directional bases and image compression performances.