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Subband finite state scalar quantization.

T Naveen1, J W Woods

  • 1Center for Image Process. Res., Rensselaer Polytech. Inst., Troy, NY.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
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This study introduces a novel image-coding algorithm that reduces quantization noise. The new method utilizes local variance and uniform threshold quantizers for improved image compression performance.

Area of Science:

  • Digital image processing
  • Signal processing
  • Computer vision

Background:

  • Image compression is crucial for efficient data transmission and storage.
  • Existing image-coding algorithms face challenges in minimizing quantization noise.
  • Exploiting inter-subband relationships can potentially enhance compression efficiency.

Purpose of the Study:

  • To develop a new image-coding algorithm.
  • To reduce quantization noise in digital images.
  • To improve image compression performance compared to existing methods.

Main Methods:

  • The proposed algorithm exploits relationships between image subbands.
  • Local variance is used for state selection.
  • Uniform threshold quantizers are applied to subbands.

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Main Results:

  • The algorithm achieved a reduction in quantization noise.
  • Performance was evaluated on the Lena image.
  • Results show improvement over other published techniques at various bit rates.

Conclusions:

  • The novel image-coding algorithm effectively reduces quantization noise.
  • The method offers superior performance for image compression.
  • Exploiting subband relationships is a promising approach for image coding.