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Oriented wavelet transform for image compression and denoising
Vivien Chappelier1, Christine Guillemot
1IRISA/University of Rennes 1, France. vivien.chappelier@free.fr
Summary
This study presents a novel wavelet-based image processing transform using adaptive local orientations for enhanced energy compaction. It shows improved performance in image compression and denoising applications.
Area of Science:
- Image Processing
- Signal Analysis
- Computer Vision
Background:
- Traditional wavelet transforms offer multiscale analysis but can lack directional adaptivity.
- The lifting paradigm enables efficient and reversible wavelet construction.
- Existing methods for image compression and denoising have limitations in capturing local image structures.
Purpose of the Study:
- Introduce a new image processing transform leveraging wavelets and the lifting paradigm.
- Enhance energy compaction and adaptivity for improved image coding and denoising.
- Investigate the properties and performance of the proposed transform against existing methods.
Main Methods:
- Applied unidimensional wavelet lifting steps along adaptively chosen local orientations on a quincunx grid.
- Iterated decomposition for fine-grained multiscale analysis.
- Utilized quad-tree coding for the orientation map and joint rate-distortion optimization for image compression.
- Employed a Markov model for orientation extraction in image denoising.
Main Results:
- Achieved adaptive orientation selection to minimize prediction error, maximizing energy compaction.
- Demonstrated preserved wavelet properties like regularity and orthogonality due to the lifting scheme.
- Evaluated superior rate-distortion performance in image coding and effective denoising capabilities compared to other transforms.
Conclusions:
- The novel wavelet transform offers significant advantages for image processing tasks.
- Adaptive orientation selection and the lifting scheme contribute to high performance in compression and denoising.
- This transform provides a powerful new tool for advanced image analysis and manipulation.
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