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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Conventional image compression methods primarily focus on rate-distortion, often neglecting semantic understanding and perception quality, particularly at low bitrates.
    • Current codecs underutilize the high-level semantic understanding humans naturally employ for image interpretation and generation.
    • Downstream computer vision algorithms, a growing consumer group for compressed images, often have their performance overlooked by traditional codecs.

    Purpose of the Study:

    • To present a generic framework enabling any image codec to incorporate high-level semantic information.
    • To investigate the joint optimization of perception quality and distortion in image compression.
    • To develop semantic-aware codecs that enhance both visual perception and the performance of computer vision tasks.

    Main Methods:

    • A generic framework is proposed to augment low-level visual features with high-level semantics for any given image codec.
    • A novel three-phase training scheme is introduced to optimize rate-perception-distortion (R-PD) performance.
    • Semantic information is leveraged to create new, semantic-aware image codecs.

    Main Results:

    • The developed semantic-aware codecs demonstrate improved rate-perception-distortion performance.
    • Significant enhancements in perception quality are observed, especially in the low bitrate regime.
    • The semantic-aware codecs also boost the performance of downstream computer vision algorithms, validated by extensive empirical evaluations.

    Conclusions:

    • Integrating semantic understanding into image codecs offers a powerful approach to enhance compression efficiency and quality.
    • Semantic-aware codecs provide a dual benefit, improving both human viewing experience and machine-based image analysis.
    • The proposed framework and training scheme offer a viable path towards more intelligent and versatile image compression technologies.