BFrWF: Block-based FrWF for coding of high-resolution images with memory-complexity constrained -devices

A typical image coder generally consists of a transform stage followed by quantization and coding stages. The memory requirement of an image coder would be the maximum of both the stages and complexity would be the sum of both stages. Due to large memory requirements, most of the existing image coders are unsuitable for their implementation on memory-constrained-platforms especially for high-resolution images. In this paper, we propose a low memory approach, Block-based Fractional Wavelet Filter (BFrWF), to compute wavelet transform coefficients of high-resolution images. Furthermore, BFrWF can be combined with low memory wavelet-based image coding algorithms to design low-memory image codec. Evaluation results show that the BFrWF requires less than 10 kB of RAM (available over most of the low-cost sensor nodes) even for high-resolution (HR) images, thus making it suitable for visual sensor networks. Moreover, the proposed BFrWF implemented with 8 blocks has 25.64^% less complexity than FrWF and 80.45% less complexity than segmented FrWF (SFrWF) implemented with partitioning of an image line into 8 segments (SFrWF - a low memory variant of FrWF) for HR-image of dimension 2048×2048.