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Related Experiment Video

Updated: Jul 10, 2025

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Joint Learning of Fully Connected Network Models in Lifting Based Image Coders.

Tassnim Dardouri, Mounir Kaaniche, Amel Benazza-Benyahia

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 21, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel multi-scale optimization technique for jointly learning Fully Connected Neural Network (FCNN) models in lifting-based image coding. This approach enhances both lossy and lossless image compression performance.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Lifting-based image coding schemes rely heavily on optimizing prediction and update operators.
    • Fully Connected Neural Networks (FCNNs) offer a recent advancement in these lifting structures.

    Purpose of the Study:

    • To develop a method for jointly learning FCNN-based prediction and update models in lifting structures.
    • To improve the efficiency and effectiveness of image compression algorithms.

    Main Methods:

    • A statistical model-based entropy loss function was developed to approximate coding rates.
    • A multi-scale optimization technique was introduced to simultaneously learn all FCNN models.
    • Two distinct loss functions were investigated across different resolution levels: one combining standard losses and another approximating the rate-distortion criterion.

    Main Results:

    • The proposed multi-scale optimization technique effectively learns FCNN models simultaneously.
    • Experimental results demonstrated significant benefits for both lossy and lossless image compression.
    • The developed loss functions provide good approximations for coding rate and rate-distortion.

    Conclusions:

    • Jointly learning FCNN models using multi-scale optimization is more effective than separate learning.
    • The proposed approach offers a promising direction for advanced image compression techniques.
    • The method shows practical advantages on standard image datasets for various compression scenarios.