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Generation and Coherent Control of Pulsed Quantum Frequency Combs
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Context modeling based on context quantization with application in wavelet image coding.

Jianhua Chen1

  • 1Department of Electronic Engineering, Yunnan University, Kunming 650091, China. chenjh@ynu.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 21, 2004
PubMed
Summary
This summary is machine-generated.

Context quantization optimizes image compression by addressing the high cost of complex models. This method, similar to vector quantization, improves coding performance in embedded wavelet schemes.

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Area of Science:

  • Computer Science
  • Signal Processing
  • Information Theory

Background:

  • Context modeling enhances image compression but incurs significant computational cost with high-order models.
  • Context quantization offers an efficient solution to mitigate the model cost in image coding.

Purpose of the Study:

  • To analyze the general context quantization problem and its relation to vector quantization.
  • To develop an optimal context quantizer using a Lloyd-style iterative algorithm.
  • To apply the proposed context quantization strategy to an embedded wavelet coding scheme.

Main Methods:

  • Detailed analysis of the context quantization problem, drawing parallels to vector quantization.
  • Design of an optimal context quantizer via a Lloyd-style iterative algorithm with a defined distortion measure.
  • Integration of the context quantization strategy into an embedded wavelet coder for significance and sign symbol coding.

Main Results:

  • Context quantization is demonstrated to be analogous to vector quantization.
  • An optimal context quantizer is achievable through an iterative algorithm.
  • The proposed strategy yields good coding performance in an embedded wavelet coding scheme.

Conclusions:

  • Context quantization effectively balances compression gain and model cost.
  • The Lloyd-style iterative algorithm provides an optimal solution for context quantizer design.
  • The application in embedded wavelet coding demonstrates practical efficacy and improved performance.