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Image compression using the 2-D wavelet transform.

A S Lewis1, G Knowles

  • 1Dept. of Electr. Eng., Imperial Coll., London.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1992
PubMed
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This study introduces an image compression algorithm using wavelet transforms and human visual system (HVS) noise sensitivity. It achieves high-quality image reconstruction at low bit rates, suitable for VLSI implementation.

Area of Science:

  • Digital Image Processing
  • Computer Vision
  • Signal Processing

Background:

  • Image compression is crucial for efficient data storage and transmission.
  • Wavelet transforms offer effective multi-resolution image analysis.
  • Human visual perception plays a key role in perceptually lossless compression.

Purpose of the Study:

  • To develop a novel image compression algorithm.
  • To leverage the 2-D orthogonal wavelet transform for spatial and spectral localization.
  • To incorporate human visual system (HVS) noise sensitivity for optimized quantization.

Main Methods:

  • Decomposition of images using 2-D orthogonal wavelet transform.
  • Hierarchical and individual coding of transformed coefficients.

Related Experiment Videos

  • Quantization based on locally estimated HVS noise sensitivity.
  • VLSI (Very Large-Scale Integration) mapping feasibility assessment.

    • Main Results:

    • High to acceptable quality reconstruction achieved for standard test images (Miss America, Lena).
    • Significant compression ratios obtained: 0.3-0.2 bits per pixel (bpp) for Miss America and 0.64-0.43 bpp for Lena.
    • Algorithm demonstrates suitability for hardware implementation.

    Conclusions:

    • The proposed wavelet-based compression algorithm effectively utilizes HVS characteristics.
    • Achieves high compression ratios with good image fidelity.
    • Presents a viable solution for efficient image compression, particularly for VLSI applications.