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<p>Image vectorization transforms raster images into scalable vector graphics, offering advantages in resolution independence and compact representation. Traditional and modern machine learning-based methods face challenges in redundancy, structural clarity, computational costs, and editability. To address these, we propose ALIV (Adaptive Layered Image Vectorization), a novel two-step framework combining comprehensive color quantization pretreatment with a machine learning vectorization process. ALIV enhances shape representation through adaptive polygonal primitive initialization and employs structural and curve loss functions to maintain clear boundaries and reduce disordered curves. Our approach achieves high fidelity, superior structural quality, and enhanced editability while maintaining computational efficiency. Experiments demonstrate ALIV’s superiority over state-of-the-art methods, significantly reducing computational overhead and ensuring better vectorization results. This work contributes to the field of computer graphics by providing a practical and efficient solution for image vectorization. The source code for ALIV is publicly available at <a href="https://github.com/SamEr32/ALIV">https://github.com/SamEr32/ALIV</a> (DOI:10.5281/zenodo.17311513).</p>

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Enhancing image vectorization fidelity and editability through adaptive layered techniques

  • 期刊论文

  • 发表日期：

    2026年3月12日

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  • Qing Xie,
  • Guixiang Nie,
  • Anshu Hu,
  • Yanchun Ma,
  • Jinyu Xu,
  • Jiachen Li

摘要

Image vectorization transforms raster images into scalable vector graphics, offering advantages in resolution independence and compact representation. Traditional and modern machine learning-based methods face challenges in redundancy, structural clarity, computational costs, and editability. To address these, we propose ALIV (Adaptive Layered Image Vectorization), a novel two-step framework combining comprehensive color quantization pretreatment with a machine learning vectorization process. ALIV enhances shape representation through adaptive polygonal primitive initialization and employs structural and curve loss functions to maintain clear boundaries and reduce disordered curves. Our approach achieves high fidelity, superior structural quality, and enhanced editability while maintaining computational efficiency. Experiments demonstrate ALIV’s superiority over state-of-the-art methods, significantly reducing computational overhead and ensuring better vectorization results. This work contributes to the field of computer graphics by providing a practical and efficient solution for image vectorization. The source code for ALIV is publicly available at https://github.com/SamEr32/ALIV (DOI:10.5281/zenodo.17311513).