- Full Description
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.
- Table of Contents
Table of Contents
- Filter Banks and DWT.
- Finite Precision Error Modeling and Analysis.
- PVM Implementation of DWT
- Based Image Denoising.
- Based Power Quality Classification.
- Conclusions and Future Directions.
If you think that you've found an error in this book, please let us know by emailing to firstname.lastname@example.org . You will find any confirmed erratum below, so you can check if your concern has already been addressed. No errata are currently published