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Image coding using adaptive recursive interpolative DPCM.

E A Gifford1, B R Hunt, M W Marcellin

  • 1Dept. of Electr. and Comput. Eng., Arizona Univ., Tucson, AZ.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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This study introduces an efficient image coding method using predictive coding and adaptive techniques. The developed image coder achieves high signal-to-noise ratios comparable to advanced transform coders with reduced decoder complexity.

Area of Science:

  • Digital image processing
  • Data compression
  • Signal processing

Background:

  • Advanced image coding techniques are crucial for efficient data transmission and storage.
  • Minimizing decoder complexity is a key challenge in developing practical image compression systems.

Purpose of the Study:

  • To present a novel predictive image coder with significantly reduced decoder complexity.
  • To achieve high signal-to-noise ratios (SNRs) competitive with state-of-the-art transform coders.

Main Methods:

  • Utilized recursive interpolative Differential Pulse Code Modulation (DPCM).
  • Incorporated adaptive classification for improved coding efficiency.
  • Employed entropy-constrained trellis coded quantization.
  • Implemented optimal rate allocation strategies.

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Main Results:

  • The proposed image coder demonstrates minimal decoder complexity.
  • Achieved signal-to-noise ratios (SNRs) comparable to advanced transform coders.
  • The combination of techniques resulted in high-fidelity image compression.

Conclusions:

  • The developed predictive image coder offers a compelling balance between compression performance and decoder simplicity.
  • This approach provides a viable alternative for applications requiring efficient image compression with low computational overhead.