Memory efficient inverse DWT computation of HR-images for WVSNs/IoT

In this paper, a novel low memory technique for computing inverse discrete wavelet transform (IDWT) of highresolution (HR) images is proposed. The proposed technique needs to store only one subband line in system's memory at a time for computing IDWT of images. Moreover, lifting scheme is also incorporated in the proposed method in order to reduce its complexity. Experimental results demonstrate that the proposed method (without lifting) requires approximately 99.90%, 80%, and 83.88% less memory than the conventional IDWT, line-based IDWT (with 5/3 filter-bank), and line-based IDWT (with 9/7 filterbank). Also, the proposed method (with lifting) requires 77.78% and 84.62% less memory than the lifting line-based IDWT with 5/3 and 9/7 filter-banks respectively. It is also shown through simulations that the proposed method (with and without lifting) can be combined with state-of-the-art wavelet-based image coding algorithms without degrading their performance. Thus, the proposed method (with and without lifting) is suitable for computing the inverse DWT of images over memory-constrained wireless visual sensor networks (WVSNs)/Internet of things (IoT).

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