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Performance evaluation of the fractional wavelet filter: A low-memory image wavelet transform for multimedia sensor networks

Performance evaluation of the fractional wavelet filter: A low-memory image wavelet transform for multimedia sensor networks,10.1016/j.adhoc.2010.08.0

Performance evaluation of the fractional wavelet filter: A low-memory image wavelet transform for multimedia sensor networks   (Citations: 1)
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Existing image wavelet transform techniques exceed the computational and memory resources of low-complexity wireless sensor nodes. In order to enable multimedia wireless sensors to use image wavelet transforms techniques to pre-process collected image sensor data, we introduce the fractional wavelet filter. The fractional wavelet filter computes the wavelet transform of a 256×256 grayscale image using only 16-bit fixed-point arithmetic on a micro-controller with less than 1.5kbyte of RAM. We comprehensively evaluate the resource requirements (RAM, computational complexity, computing time) as well as image quality of the fractional wavelet filter. We find that the fractional wavelet transform computed with fixed-point arithmetic gives typically negligible degradations in image quality. We also find that combining the fractional wavelet filter with a customized wavelet-based image coding system achieves image compression competitive to the JPEG2000 standard.
Journal: Ad Hoc Networks , vol. 9, no. 4, pp. 482-496, 2011
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    • ...of a two-dimensional image was developed in [15], [27]...
    • ...For a detailed performance evaluation that comprehensively evaluates the resource requirements for the fractional wavelet filter and its image quality we refer to [27]...
    • ...We briefly note that a detailed computational complexity analysis [27] revealed that the fractional wavelet filter without the lifting scheme requires about 2.9 times more add operations and 3.25 times more multiply operations than the classical convolution approach (which requires memory for 2N 2 pixels with four Bytes per pixel for floating-point and two Bytes per pixel for fixed-point computations)...

    Stephan Reinet al. Low-Memory Wavelet Transforms for Wireless Sensor Networks: A Tutorial

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