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Multiresolution multiresource progressive image transmission.

W J Hwang1, H Derin

  • 1Dept. of Electr. and Comput. Eng., Massachusetts Univ., Amherst, MA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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This study introduces a progressive image transmission (PIT) algorithm optimizing image quality and resource allocation using wavelet transforms and tree-structured vector quantization (TSVQ). The method allows prespecified resolution and resource management at each stage for efficient image delivery.

Area of Science:

  • Digital Image Processing
  • Data Compression
  • Computer Vision

Background:

  • Progressive image transmission (PIT) is crucial for efficient data delivery over networks.
  • Existing PIT methods often lack flexibility in resource allocation and resolution control.
  • Wavelet transform and vector quantization are established techniques in image compression.

Purpose of the Study:

  • To develop a novel progressive image transmission (PIT) design algorithm.
  • To enable prespecification of resolution and resources (rate/distortion, storage) at each transmission stage.
  • To optimize image quality and transmission efficiency under resource constraints.

Main Methods:

  • Utilizes the wavelet transform to create a pyramid image representation.

Related Experiment Videos

  • Employs tree-structured vector quantization (TSVQ) for each subimage.
  • Applies resource allocation strategies to optimize performance at each stage under constraints.

    • Main Results:

    • The algorithm allows for successive refinement of image subregions.
    • Resource allocation is optimized for current stage performance within specified constraints.
    • Resolution can be tailored based on application needs or design goals.

    Conclusions:

    • The proposed PIT algorithm offers enhanced control over image quality and resource usage.
    • Integration of wavelet transform and TSVQ provides a robust framework for progressive image transmission.
    • The method is adaptable for various applications requiring efficient and high-quality image delivery.