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Integer wavelet transform for embedded lossy to lossless image compression
J Reichel1, G Menegaz, M J Nadenau
1Signal Processing Laboratory, Swiss Federal Institute of Technology, 1015 Lausanne, Switzerland. reichel@ieee.org
Summary
The integer wavelet transform (IWT) offers similar compression performance to the discrete wavelet transform (DWT) for lossy image compression. This study models IWT
Area of Science:
- Digital Image Processing
- Signal Processing
- Data Compression
Background:
- Discrete Wavelet Transform (DWT) is a standard for embedded lossy image compression.
- Lifting Scheme (LS) enables perfect reconstruction and nonlinear transforms, leading to Integer Wavelet Transform (IWT) for efficient lossless compression.
- IWT presents an alternative to DWT with comparable rate-distortion performance.
Purpose of the Study:
- To theoretically investigate and model the degradations introduced by using IWT instead of DWT in lossy image compression.
- To analyze the impact of rounding operations in IWT on reconstructed image quality.
Main Methods:
- Developed a theoretical framework to model degradations caused by IWT in lossy compression.
- Modeled rounding operations in IWT as additive noise.
- Propagated the modeled noise through the Lifting Scheme structure to assess its impact on reconstructed pixels.
Main Results:
- The proposed noise propagation model accurately predicts results obtained with the EZW algorithm for image compression using IWT.
- Experimental comparisons quantified differences in bit rate and visual quality between IWT and DWT for lossy compression.
Conclusions:
- The study provides a theoretical understanding of IWT's impact on lossy image compression quality.
- The developed model aids in predicting and managing compression artifacts introduced by IWT.
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