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    Area of Science:

    • Computer Vision
    • Image Processing
    • Information Theory

    Background:

    • Traditional image coding focuses separately on human vision (image compression) and machine vision (feature compression).
    • Existing joint human-machine vision approaches lack clarity on image-feature correlations.
    • Deep networks offer powerful tools for generating structural image representations.

    Purpose of the Study:

    • To develop a unified image coding framework for joint human-machine vision.
    • To leverage deep network features for scalable and semantically rich image representations.
    • To improve compression efficiency for applications requiring both visual fidelity and machine understanding.

    Main Methods:

    • Utilized deep networks to generate structural image representations from deeper to shallower layers, forming an entropy-decreasing series.
    • Developed the Structural Scalable Semantic Image Coding (SSSIC) framework for embedded bitstreams.
    • Implemented SSSIC using coarse-to-fine image classification, extended to object detection and instance segmentation.

    Main Results:

    • The SSSIC framework successfully generated embedded bitstreams decodable for semantic analysis or full human vision.
    • Experimental results validated the effectiveness of the SSSIC framework.
    • The exemplar SSSIC scheme demonstrated higher compression efficiency than separate image and feature compression methods.

    Conclusions:

    • Deep network features provide a scalable approach for joint image and feature compression.
    • The SSSIC framework offers a flexible solution for diverse human-machine vision tasks.
    • This approach enhances compression efficiency and semantic relevance in image coding.