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    Area of Science:

    • Image processing and computer vision.
    • Digital signal processing.
    • Wavelet theory and applications.

    Background:

    • Spectral-spatial transforms (SSTs) are crucial for converting raw camera images from Color Filter Array (CFA) sampled formats into decorrelated color spaces like YCbCr.
    • Existing SST methods offer improvements but advancements in wavelet-based transforms can further optimize image compression efficiency.

    Purpose of the Study:

    • To introduce and evaluate novel wavelet-based spectral-spatial transforms (WSSTs) for improved CFA-sampled image compression.
    • To compare the performance of WSSTs using different wavelet filters against existing SSTs and no transform methods.

    Main Methods:

    • Development of three types of macropixel SST (MSST) within 2x2 macropixels.
    • Integration of Cohen-Daubechies-Feauveau (CDF) 5/3 and 9/7 wavelet transforms within MSSTs, replacing Haar and Haar-like transforms.
    • Evaluation of WSSTs in lossless and lossy image compression scenarios using the JPEG 2000 standard.

    Main Results:

    • WSSTs improved bitrates by 1.67-3.17% in lossless compression compared to no transform.
    • WSSTs with 5/3 wavelets offered a 0.31-0.71% bitrate improvement over existing SSTs in lossless compression.
    • In lossy compression, WSSTs showed significant gains (2.25-4.40 dB BD-PSNR and 26.04-49.35% BD-rate reduction) versus no transform.
    • WSSTs with 9/7 wavelets yielded 0.13-0.40 dB BD-PSNR and 2.27-4.80% BD-rate improvements over the best existing SSTs in lossy compression.

    Conclusions:

    • Wavelet-based spectral-spatial transforms offer a significant advancement in CFA-sampled image compression.
    • The proposed WSSTs, particularly those employing CDF 5/3 and 9/7 wavelets, provide superior compression efficiency in both lossless and lossy applications.
    • These findings highlight the potential of advanced wavelet transforms for optimizing image compression pipelines.