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Published on: February 15, 2022
Subband image coding using watershed and watercourse lines of the wavelet transform.
1Dept. of Syst. Design Eng., Waterloo Univ., Ont.
Summary
This study introduces primitive-based image coding using nonorthogonal dyadic wavelets for efficient data compression. The method achieves significant data reduction with minimal fidelity loss for grayscale images.
Area of Science:
- Signal Processing
- Image Compression
- Wavelet Theory
Background:
- Traditional image coding methods face challenges in balancing compression ratios and fidelity.
- Nonorthogonal dyadic wavelets offer a promising approach for advanced image representation.
Purpose of the Study:
- To develop and evaluate a primitive-based image coding technique using nonorthogonal dyadic wavelets.
- To achieve high data reduction rates for grayscale images with minimal loss of visual information.
Main Methods:
- Utilizing a 3D isotropic wavelet to approximate the difference-of-Gaussians (D-o-G) operator.
- Generating multiscale band-pass signals and a low-pass subband signal for image reconstruction.
- Employing watershed/watercourse lines for image representation and data compression.
Main Results:
- Demonstrated image recovery from watershed/watercourse lines and the low-pass subband.
- Achieved data reduction between 0.1 and 0.3 bits per pixel for grayscale images.
- Showcased minimal fidelity loss through thresholding, subsampling, and edge post-emphasis.
Conclusions:
- Primitive-based image coding with nonorthogonal dyadic wavelets is effective for high-fidelity compression.
- The proposed method offers a viable alternative for efficient grayscale image coding.
- Further compression is attainable via chain coding and predictive coding of image features.
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