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Efficient Algorithms for Discrete Wavelet Transform

SBCS: EFFICIENT ALGORITHMS FOR DISCRETE WAVELET TRNSFRM

Information Technology

Computing & Information Technology

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B2B GT

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B2B & B2C

Due to its inherent time-scale locality characteristics, the discrete wavelet transform (DWT) has received considerable attention in signal/image processing. Wavelet transforms have excellent energy compaction characteristics and can provide perfect reconstruction. The shifting (translation) and scaling (dilation) are unique to wavelets. Orthogonality of wavelets with respect to dilations leads to multigrid representation. As the computation of DWT involves filtering, an efficient filtering process is essential in DWT hardware implementation. In the multistage DWT, coefficients are calculated recursively, and in addition to the wavelet decomposition stage, extra space is required to store the intermediate coefficients. Hence, the overall performance depends significantly on the precision of the intermediate DWT coefficients. This work presents new implementation techniques of DWT, that are efficient in terms of computation, storage, and with better signal-to-noise ratio in the reconstructed signal.

POST SECONDARY FOUR YEAR

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COLSR

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Other Frontmatter.
Title Page.
Copyright Page.
Preface.
Contents.
Symbols and Notations.
1: Introduction.
2: Filter Banks and DWT.
3: Finite Precision Error Modeling and Analysis.
4: PVM Implementation of DWT-Based Image Denoising.
5: DWT-Based Power Quality Classification.
6: Conclusions and Future Directions.
Bibliography.

Efficient Algorithms for Discrete Wavelet Transform.css-1p8rvzy{position:absolute;padding-left:8px;}

Efficient Algorithms for Discrete Wavelet Transform