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Related Experiment Video

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Exploration of Learned Lifting-Based Transform Structures for Fully Scalable and Accessible Wavelet-Like Image

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    This study integrates neural networks into wavelet-like transforms for image compression. Retaining fixed lifting steps and using more channels in learned operators significantly improves performance, achieving over 25% bit-rate savings compared to JPEG 2000.

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    Area of Science:

    • Image processing
    • Machine learning
    • Signal processing

    Background:

    • Traditional wavelet transforms are foundational for image compression.
    • Neural networks offer potential for enhancing transform-based compression methods.
    • Scalable and accessible image compression remains an active research area.

    Purpose of the Study:

    • To comprehensively study the integration of neural networks into lifting-based wavelet-like transforms.
    • To evaluate the impact of various network architectures and lifting step configurations on image compression performance.
    • To identify optimal strategies for learned lifting operators in image compression.

    Main Methods:

    • Exploration of different lifting step arrangements and neural network architectures for learned lifting operators.
    • Analysis of parameters such as the number of learned lifting steps, channels, layers, and kernel support.
    • Investigation of two generic training methodologies suitable for diverse lifting structures.

    Main Results:

    • Retaining fixed lifting steps from the base wavelet transform proved highly beneficial.
    • Increased learned lifting steps and layers did not significantly enhance compression performance.
    • Utilizing more channels within learned lifting operators yielded performance benefits.
    • The proposed learned wavelet-like transform achieved over 25% bit-rate savings compared to JPEG 2000.

    Conclusions:

    • Fixed lifting steps are crucial for effective learned wavelet-like transforms.
    • Network depth (layers) is less critical than channel width for compression performance.
    • The developed learned wavelet-like transform offers a significant improvement in image compression efficiency.