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Related Experiment Videos

Visibility of wavelet quantization noise.

A B Watson1, G Y Yang, J A Solomon

  • 1NASA Ames Research Center, Moffett Field, CA 94035, USA. beau@vision.arc.nasa.gov

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|August 1, 1997
PubMed
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Quantifying discrete wavelet transform (DWT) quantization errors is key for optimal image compression. This study models DWT noise detection thresholds to enable perceptually lossless compression by minimizing visible artifacts.

Area of Science:

  • Image processing
  • Computer vision
  • Perceptual coding

Background:

  • Discrete Wavelet Transform (DWT) is crucial for image compression, decomposing images into frequency and orientation bands.
  • Quantization errors in DWT can lead to visible artifacts, necessitating measures of their visibility for effective compression.
  • Understanding DWT uniform quantization noise, an artifact from quantizing DWT coefficients, is essential for optimizing compression algorithms.

Purpose of the Study:

  • To measure visual detection thresholds for DWT uniform quantization noise across different color channels and orientations.
  • To develop a mathematical model predicting DWT noise detection thresholds based on wavelet level, orientation, and display resolution.
  • To enable the creation of perceptually lossless quantization matrices for improved image compression.
Keywords:
NASA Center ARCNASA Discipline Space Human Factors

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Main Methods:

  • Visual detection thresholds for DWT uniform quantization noise were measured in Y, Cb, and Cr color channels.
  • The spatial frequency of wavelets was defined based on display resolution and wavelet level.
  • A mathematical model was constructed to predict detection thresholds as a function of wavelet level, orientation, and display visual resolution.

Main Results:

  • Detection thresholds for DWT uniform quantization noise increase significantly with wavelet spatial frequency.
  • Thresholds vary across color channels (Y, Cr, Cb) and orientations (lowpass, horizontal/vertical, diagonal).
  • A predictive model for DWT noise detection thresholds was successfully constructed.

Conclusions:

  • The developed mathematical model allows for the calculation of perceptually lossless quantization matrices, where errors are theoretically below the visual threshold.
  • This model provides a foundation for adaptive quantization schemes in image compression.
  • Optimizing DWT-based image compression requires understanding and modeling the visibility of quantization noise across various parameters.