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Interactive Deep Colorization and its Application for Image Compression.

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    This study introduces a novel two-stage deep learning method for image colorization, enabling flexible user control over color themes and details. The approach achieves state-of-the-art results and introduces an efficient image compression scheme.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Deep learning methods show promise for grayscale image colorization.
    • Existing methods offer limited user control over colorization outputs.
    • Differentiating influences of various user inputs remains a challenge.

    Purpose of the Study:

    • To propose a two-stage deep colorization method with flexible user control.
    • To enable users to control colorization using both global and local inputs.
    • To develop an associated image compression scheme.

    Main Methods:

    • A two-stage deep colorization framework is proposed.
    • Global color themes are incorporated using K-means clustering and a global theme loss.
    • A specialized loss function differentiates input influences and prevents artifacts.
    • A color theme recommendation system is introduced.
    • An image compression scheme with variable compression ratios is developed.

    Main Results:

    • The colorization method allows flexible control over results with minimal user input.
    • State-of-the-art performance is achieved in image colorization.
    • The image compression scheme significantly improves image quality at given compression ratios.
    • The method outperforms existing state-of-the-art compression techniques.

    Conclusions:

    • The proposed method offers enhanced control and state-of-the-art results for image colorization.
    • The integrated image compression scheme provides superior quality and efficiency.
    • This work advances deep learning applications in image manipulation and compression.