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MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model.

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    Summary
    This summary is machine-generated.

    This study introduces Multimodal Image Semantic Compression (MISC), a new method using Large Multimodal Models (LMMs) to achieve high-quality image compression at ultra-low bitrates. MISC balances ground truth consistency and perceptual quality for both natural and AI-generated images.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Ultra-low bitrate image compression faces a trade-off between ground truth consistency and perceptual quality.
    • Advancements in Large Multimodal Models (LMMs) offer potential solutions for balancing these compression goals.

    Purpose of the Study:

    • To develop a novel image compression method that overcomes the limitations of existing algorithms at ultra-low bitrates.
    • To leverage LMMs for enhanced image compression, improving both fidelity and visual quality.

    Main Methods:

    • Proposes Multimodal Image Semantic Compression (MISC), integrating an LMM encoder for semantic extraction.
    • Employs a map encoder for semantic region localization and an image encoder for bitstream generation.
    • Utilizes a decoder to reconstruct images based on semantic and spatial information.

    Main Results:

    • MISC effectively compresses both Natural Sense Images (NSIs) and AI-Generated Images (AIGIs).
    • Achieves optimal consistency with ground truth and superior perceptual quality.
    • Demonstrates significant bitrate savings of up to 50% compared to existing methods.

    Conclusions:

    • MISC presents a viable solution for next-generation image storage and communication systems.
    • The method successfully balances semantic understanding and visual reconstruction for efficient compression.
    • Offers a promising approach for handling diverse image content, including emerging AIGIs.