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    This study introduces aiWave, a novel volumetric image compression framework using a 3-D trained wavelet-like transform. aiWave offers improved performance over existing methods like JP3D and HEVC for biological and clinical imaging.

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    Area of Science:

    • Medical imaging
    • Image processing
    • Data compression

    Background:

    • Volumetric image compression is crucial for biological research and clinical practice.
    • Current methods like JP3D, based on fixed wavelet transforms, have performance limitations.
    • Existing methods struggle with signal-dependent and non-separable characteristics of volumetric data.

    Purpose of the Study:

    • To develop an advanced volumetric image compression method.
    • To improve compression efficiency and performance for biological and clinical images.
    • To create an adaptive compression scheme for diverse volumetric datasets.

    Main Methods:

    • Designed a 3-D trained wavelet-like transform for signal-dependent and non-separable transformations.
    • Introduced an affine wavelet basis to capture regional correlations in volumetric images.
    • Developed an end-to-end compression framework, aiWave, incorporating the new transform and weight sharing strategies.

    Main Results:

    • aiWave with a factorized entropy coder outperforms JP3D with comparable complexity.
    • aiWave achieves significantly better performance than HEVC when a context module is added.
    • The proposed method demonstrates superior volumetric image compression capabilities.

    Conclusions:

    • The novel 3-D affine wavelet-like transform enhances volumetric image compression.
    • aiWave provides an adaptive and efficient solution for transmitting and storing volumetric data.
    • This framework offers a promising advancement for medical and biological imaging applications.