2-D DWT System Architecture for Image Compression

Wavelet transform has contributed significantly in multiple areas such as image processing, compression, signal analysis, and medical imaging. Discrete wavelet transform (DWT) requires very large memory requirement and is computationally intensive, especially for 2-D transform that has a quadratic computational complexity. In this paper, we propose a dedicated processor for 2-D DWT computation. The DWT system architecture is parameterizable, where its performance can be scaled by increasing or reducing the DWT engines, according to different application needs. This architecture requires significantly less computational resources and internal memory. The proposed architecture can achieve a theoretical throughput of 138 frames per second for a 2048 × 1536 video processing. The DWT system has been designed for scalability to support up to 8 parallel DWT engines.

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