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Generalized block-lifting factorization of M-channel biorthogonal filter banks for lossy-to-lossless image coding.
Taizo Suzuki1, Masaaki Ikehara, Truong Q Nguyen
1Department of Electronics and Electrical Engineering, Keio University, Yokohama 223-8522, Japan. ikehara@tkhm.elec.keio.ac.jp
Summary
This study introduces a generalized block-lifting factorization for M-channel biorthogonal filter banks (BOFBs) in image coding. This advanced method offers higher coding gains and improved visual quality compared to existing techniques.
Area of Science:
- Digital Signal Processing
- Image Compression
- Filter Bank Design
Background:
- Conventional lifting factorizations for biorthogonal filter banks (BOFBs) often impose restrictions like paraunitariness and specific McMillan degrees.
- These restrictions limit the flexibility and performance of filter banks in lossy-to-lossless image coding applications.
Purpose of the Study:
- To present a generalized block-lifting factorization for M-channel (M > 2) BOFBs.
- To develop more flexible and higher-performing filter banks for lossy-to-lossless image coding.
- To improve both objective measures and perceptual visual quality in image compression.
Main Methods:
- Developed a generalized block-lifting factorization for M-channel BOFBs.
- Designed several novel BOFBs based on the proposed structure.
- Applied the designed BOFBs to image coding tasks.
Main Results:
- The proposed block-lifting structure is more general and imposes fewer restrictions than conventional methods.
- Achieved higher coding gains compared to previous lifting factorization techniques.
- Demonstrated superior performance in objective measures and perceptual visual quality for images with high-frequency content when compared to JPEG 2000 (5/3 and 9/7 DWT) and JPEG XR (4x8 HLT).
Conclusions:
- The generalized block-lifting factorization offers significant advantages for M-channel BOFBs in image coding.
- The proposed BOFBs provide enhanced performance, particularly for images rich in high-frequency details.
- This approach represents a notable advancement in lossy-to-lossless image compression technology.
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