embeddedzerotreewavelet (EZW) algorithm is the well-known effective coding technique for low-bit-rate image compression. In this study, the authors propose a modification of this algorithm, namely new enhanced EZW (...
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embeddedzerotreewavelet (EZW) algorithm is the well-known effective coding technique for low-bit-rate image compression. In this study, the authors propose a modification of this algorithm, namely new enhanced EZW (NE-EZW), allowing to achieve a high compression performance in terms of peak-signal-to-noise ratio and bitrate for lossy image compression. To distribute probabilities in a more efficient way, the proposed approach is based on increasing the number of coefficients not to be encoded by the use of new symbols. Furthermore, the proposed method optimises the binary coding by the use of the compressor cell operator. Experimental results demonstrated the effectiveness of the proposed scheme over the conventional EZW and other improved EZW schemes for both natural and medical image coding applications. They have also shown that the proposed approach outperforms the most well-known algorithms, namely set partitioning in hierarchical trees (SPIHT) and JPEG2000.
A novel signal compression and reconstruction procedure suitable for guided wave based structural health monitoring (SHM) applications is presented. The proposed approach combines the wavelet packet transform and freq...
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A novel signal compression and reconstruction procedure suitable for guided wave based structural health monitoring (SHM) applications is presented. The proposed approach combines the wavelet packet transform and frequency warping to generate a sparse decomposition of the acquired dispersive signal. The sparsity of the signal in the considered representation is exploited to develop data compression strategy based on the Best-Basis Compressive sensing (CS) theory. The proposed data compression strategy has been compared with the transform encoder based on the embeddedzerotree (EZT), a well known data compression algorithm. These approaches are tested on experimental Lamb wave signals obtained by acquiring acoustic emissions in a 1 m(2) aluminum plate with conventional piezoelectric sensors. The performances of the two methods are analyzed by varying the compression ratio in the range 40-80%, and measuring the discrepancy between the original and the reconstructed signal. Results show the improvement in signal reconstruction with the use of the modified CS framework with respect to transform-encoders such as the EZT algorithm with Huffman coding. (C) 2015 Elsevier Inc. All rights reserved.
A novel Compressed Sensing (CS) procedure is presented in this study for dispersive guided wave propagation analysis in passive structure health monitoring applications. The proposed approach combines the wavelet Pack...
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ISBN:
(纸本)9780819494788
A novel Compressed Sensing (CS) procedure is presented in this study for dispersive guided wave propagation analysis in passive structure health monitoring applications. The proposed approach combines the wavelet Packet multiresolution analysis, best basis selection and coefficients thresholding to generate a sparse but accurate time-frequency representation of the acquired dispersive signal, with the CS framework to efficiently compress Lamb waves signals. This approach is tested on experimental data obtained by passive excitation in a 1 m square aluminum plate and acquiring the dispersive signal with a conventional piezoelectric sensor. The proposed algorithm performance is analysed in term of compression ratio and percent residual difference. Results show the improvement in signal reconstruction with the use of the modified CS framework respect to the EZW coding, and the robustness of the proposed approach to additive noise in transmission.
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