FrWF-Based LMBTC: Memory-Efficient Image Coding for Visual Sensors

After the successful development of JPEG2000, many state-of-the-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 requirement of 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. The LMBTC is a listless form of the wavelet block tree coding 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-the-art coders. The FrWF combined with the LMBTC is, thus, a viable option for image communication over wireless sensor networks.