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Image interpolation for progressive transmission by using radial basis function networks.

T Sigitani1, Y Iiguni, H Maeda

  • 1Department of Communications Engineering, Graduate School of Engineering, Osaka University, Suita, 565-0871, Japan.

IEEE Transactions on Neural Networks
|February 7, 2008
PubMed
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This study introduces a novel hierarchical image coding method using a radial basis function network (RBFN) for efficient progressive image transmission. The RBFN-based approach enhances image quality and transmission speed without blocking artifacts.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Hierarchical image coding is crucial for efficient progressive transmission.
  • Existing methods often suffer from blocking effects and high computational costs.
  • Radial Basis Function Networks (RBFNs) offer potential for image interpolation and coding.

Purpose of the Study:

  • To apply a radial basis function network (RBFN) to hierarchical image coding for progressive transmission.
  • To develop an efficient method for computing RBFN parameters to reduce computational and memory requirements.
  • To achieve a coding method free from blocking effects and capable of rapid coarsest image generation.

Main Methods:

  • A radial basis function network (RBFN) was employed for hierarchical image coding.

Related Experiment Videos

  • The RBFN was utilized to interpolate images from subsampled versions.
  • An efficient parameter computation method was developed for the RBFN.
  • Quantization error effects were considered across decoding stages for lossless transmission.

    • Main Results:

    • The proposed RBFN-based coding method effectively generates interpolated images from subsampled data.
    • The developed parameter computation method significantly reduces computational and memory demands.
    • The coding technique successfully avoids the blocking effect common in other methods.
    • The method allows for rapid production of the coarsest image and ensures lossless progressive transmission.

    Conclusions:

    • The application of RBFN to hierarchical image coding provides an efficient solution for progressive image transmission.
    • The developed method offers advantages in terms of speed, memory efficiency, and image quality, eliminating blocking artifacts.
    • This approach facilitates lossless progressive image transmission by accounting for quantization errors across stages.