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Encoding of line drawings with a multiple grid chain code.

T Minami1, K Shinohara

  • 1Department of Electronic Engineering, Kogakuin University, Tokyo, 160, Japan.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

A new multiple grid (MG) chain code efficiently encodes line drawings using a novel grid selection and quantization method. This approach enhances encoding efficiency and line naturalness compared to existing methods.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Data Compression

Background:

  • Chain codes are widely used for representing line drawings.
  • Existing chain codes face limitations in encoding efficiency and line naturalness.
  • Optimizing quantization point allocation is crucial for effective line drawing encoding.

Purpose of the Study:

  • To propose and evaluate a novel multiple grid (MG) chain code for encoding line drawings.
  • To improve encoding efficiency, naturalness of encoded lines, and rate distortion performance.
  • To demonstrate the applicability of the MG chain code in systems like electronic blackboards.

Main Methods:

  • Development of a grid selection algorithm for targeted quantization point allocation.
  • Implementation of a labeling rule to influence code frequency.
  • Strategic quantization point placement on square sides, not corners, to enhance straight line segments.
  • Comparative performance analysis against existing chain codes.

Main Results:

  • The MG chain code demonstrates superior encoding efficiency.
  • Encoded lines exhibit enhanced naturalness.
  • The MG chain code achieves a favorable rate distortion measure.
  • The proposed method outperforms other chain codes in key performance metrics.

Conclusions:

  • The multiple grid (MG) chain code offers significant advantages for line drawing encoding.
  • Its efficiency and naturalness make it suitable for various applications.
  • The MG chain code is a promising advancement in image compression and representation.