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After the successful development of JPEG2000, many state-of-art wavelet-based image coding algorithms have been developed. However, the traditional discrete wavelet transform (DWT) is implemented with memory intensive and time consuming algorithms and therefore has very high system resource requirements. In particular, the very large required memory poses a serious limitation for multimedia applications on memory-constrained portable devices, such as digital cameras and sensor nodes. In this paper, we propose a novel wavelet-based image coder with low memory requirements and low complexity that preserves the compression efficiency. Our encoder employs the Fractional Wavelet Filter (FrWF) to calculate the DWT coefficients, which are quantized and encoded with a novel Low Memory Block Tree Coding (LMBTC) algorithm. LMBTC is a listless form of the Wavelet Block Tree Coding (WBTC) algorithm. Simulation results demonstrate that the proposed coder significantly reduces memory requirements and computational complexity and has competitive coding efficiency in comparison with other state-of-art coders. FrWF combined with LMBTC is thus a viable option for image communication over wireless sensor networks.Index Terms-Fractional wavelet filter, Low memory image codec, Visual sensors, Wireless sensor networks.

<p><span>A novel wavelet-based efficient hyperspectral image compression scheme for low memory sensors has been proposed. The proposed scheme uses the 3D dyadic wavelet transform to exploit intersubband and intrasubband correlation among the wavelet coefficients. By doing the reconstruction of the transform image cube, taking the difference between the frames, it increases the coding efficiency, reduces the memory requirement and complexity of the hyperspectral compression schemes in comparison with other state-of-the-art compression schemes.</span></p>

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