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Reversible symmetric nonexpansive convolution: an effective image boundary processing for M-channel lifting-based

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    This study introduces reversible symmetric extension for lifting-based filter banks in image compression. The method enhances both lossy and lossless coding by preserving boundary symmetry and reducing complexity.

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    Area of Science:

    • Digital image processing
    • Signal processing
    • Data compression

    Background:

    • Lifting-based linear-phase filter banks are crucial for image compression.
    • Existing boundary processing methods can introduce asymmetry and complexity.
    • Unified lossy and lossless image coding requires effective boundary handling.

    Discussion:

    • The proposed reversible symmetric extension manipulates boundary building blocks to restore symmetry lost during lifting steps.
    • Reversible symmetric nonexpansive convolution reduces computational complexity by avoiding temporary signal increases.
    • This approach ensures reversible boundary processing while maintaining high coding efficiency.

    Key Insights:

    • Achieves reversible boundary processing for M-channel lifting-based filter banks.
    • Demonstrates comparable performance to irreversible symmetric extension in lossy image coding.
    • Outperforms periodic extension in lossy-to-lossless image coding scenarios.

    Outlook:

    • Potential for improved image compression algorithms with enhanced boundary handling.
    • Further research into adaptive symmetric extension techniques.
    • Application in real-time image and video compression systems.