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High-quality image resizing using oblique projection operators.

C Lee1, M Eden, M Unser

  • 1Department of Electronic Engineering, Yonsei University, Seoul, Korea. chulhee@bubble.yonsei.ac.kr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 16, 2008
PubMed
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This study introduces a new image resizing method using oblique projection, offering faster and simpler algorithms. It achieves results comparable to optimal methods with fewer computations, improving image quality.

Area of Science:

  • Image processing
  • Computer vision
  • Digital signal processing

Background:

  • Standard image resizing uses interpolation, fitting images to continuous models.
  • Sampling theory suggests pre-filtering improves accuracy, but optimal filters are complex.
  • Optimal solutions involve orthogonal projection, difficult with high-order splines.

Purpose of the Study:

  • To develop a more practical and efficient image resizing algorithm.
  • To enable the use of higher-order models for improved image quality.
  • To achieve results comparable to optimal methods with reduced computational cost.

Main Methods:

  • Proposing an oblique projection operator instead of orthogonal projection for image resizing.
  • Utilizing B-splines for a practical implementation of the proposed method.

Related Experiment Videos

  • Comparing the oblique projection method with standard interpolation and optimal least squares solutions.

    • Main Results:

    • The oblique projection method achieves results similar to orthogonal projection.
    • Higher-order models (splines of degree n>=3) become feasible.
    • The proposed algorithm consistently outperforms standard interpolation methods.
    • Performance is comparable to the optimal least squares solution with significantly fewer computations.

    Conclusions:

    • The oblique projection method offers a practical and efficient approach to image resizing.
    • It allows for the use of advanced models, enhancing image quality.
    • The method provides a favorable trade-off between computational cost and accuracy for image scaling.