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RCodSpace: A Robust Learned Coding Method for Deep Space Visual Transmission.

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    |May 28, 2026
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    This study introduces a novel, low-complexity, and error-resilient image compression framework for deep space exploration. The new method significantly improves data transmission for missions like Mars exploration, outperforming existing techniques.

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    Area of Science:

    • Deep space exploration
    • Image compression
    • Machine learning

    Background:

    • Deep space missions require transmitting vast image data, but current methods offer limited compression and struggle with transmission errors.
    • Learning-based compression excels for natural images but is too complex and error-prone for deep space.

    Purpose of the Study:

    • To develop a novel image compression and transmission framework for deep space exploration.
    • To address the challenges of low-complexity and error resilience in deep space image coding.
    • To improve the efficiency and reliability of transmitting Martian image data.

    Main Methods:

    • Established a new, high-resolution Martian image dataset to guide network design.
    • Developed a Martian Vision Adaptive Transformation Module (MVATM) for efficient, low-complexity compression.
    • Implemented a two-stage training strategy with Joint Channel Training (JCT) for enhanced error resilience.

    Main Results:

    • The proposed framework achieves superior rate-distortion-complexity (RDC) trade-offs compared to advanced learning-based models.
    • Demonstrated significantly better performance than conventional methods in deep space simulations.
    • Hardware deployment validated the method's effectiveness and practicality.

    Conclusions:

    • The novel framework offers a viable solution for efficient and error-resilient image compression in deep space.
    • The developed techniques provide methodological insights for resource-constrained fields.
    • This advancement supports future deep space exploration by improving data transmission capabilities.