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    Area of Science:

    • Image processing
    • Digital signal processing
    • Computer vision

    Background:

    • Traditional image compression uses full RGB color, while image capture often uses Bayer CFA sensors.
    • Direct compression of CFA images avoids encoder complexity and prior color conversion.

    Purpose of the Study:

    • To develop a JPEG XS-compliant compression algorithm for direct Bayer CFA image compression.
    • To optimize image quality by understanding the CFA to full-color development process.

    Main Methods:

    • Introduced a novel Star-Tetrix transform for decorrelation.
    • Implemented a pre-emphasis function to enhance compression efficiency within the JPEG XS framework.
    • Compared performance against RGB compression workflows and other state-of-the-art CFA compression techniques.

    Main Results:

    • Achieved significant quality gains (1.5dB to over 4dB) over RGB compression.
    • Demonstrated improved complexity and quality for direct CFA compression.
    • Validated the effectiveness of the Star-Tetrix transform and pre-emphasis function.

    Conclusions:

    • Direct compression of Bayer CFA images within JPEG XS is feasible and offers superior performance.
    • The proposed methods (Star-Tetrix transform, pre-emphasis) enhance compression efficiency and image quality.
    • This approach presents a valuable alternative to traditional RGB-based image compression pipelines.