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Detecting computer-induced errors in remote-sensing JPEG compression algorithms.

Cung Nguyen1, G Robert Redinbo

  • 1Department of Electrical and Computer Engineering, University of California, Davis 95616, USA. cunguyen@ece.ucdavis.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|July 13, 2006
PubMed
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New fault tolerance methods detect soft errors in JPEG compression for remote sensing. These algorithms ensure no computer-induced errors go undetected in compressed or decompressed data.

Area of Science:

  • Computer Science
  • Image Processing
  • Fault Tolerance

Background:

  • JPEG compression is susceptible to soft errors, particularly in remote-sensing applications.
  • Existing error resilience features are insufficient against computer-induced faults.
  • Data integrity is crucial for reliable remote-sensing data.

Purpose of the Study:

  • Develop novel fault tolerance detection methods for JPEG compression.
  • Ensure all computer-induced soft errors are detected within the system.
  • Maintain JPEG output format integrity across various hardware/software implementations.

Main Methods:

  • Algorithm-level detection methods applied to major JPEG subsystems: Discrete Cosine Transform (DCT), quantizer, entropy coding, and packet assembly.
  • Utilized varied error detection techniques: real number parities (DCT), bit-level residue codes (quantizer), cyclic redundancy check (CRC) parities (entropy coding).

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  • Focused on detecting errors within subsystems and across data boundaries.

    • Main Results:

    • Verified effective detection of soft errors within JPEG subsystems.
    • Demonstrated successful detection of errors across subsystem boundaries.
    • Evaluated the impact of roundoff noise on error detection performance.

    Conclusions:

    • The developed fault tolerance methods effectively detect computer-induced soft errors in JPEG compression.
    • These methods enhance data integrity for critical applications like remote sensing.
    • The algorithm-level approach ensures broad applicability across diverse implementations.