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Related Experiment Videos

Optimal transform coding in the presence of quantization noise.

K I Diamantaras1, M G Strintzis

  • 1Dept. of Inf., Technol. Educ. Inst. of Thessaloniki, Sindos. kdiamant@it.teithe.gr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 13, 2008
PubMed
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This study introduces a new linear transform that minimizes reconstruction error, even with quantization noise. This transform outperforms the Karhunen-Loeve transform (KLT) in noisy conditions, offering improved image compression.

Area of Science:

  • Signal Processing
  • Image Compression
  • Data Transforms

Background:

  • The Karhunen-Loeve transform (KLT) minimizes reconstruction error but assumes noise-free coefficients.
  • Real-world coding systems use quantizers, introducing quantization noise.
  • Existing transforms do not account for quantization noise in their optimization.

Purpose of the Study:

  • To formulate an optimal linear transform that incorporates quantization noise.
  • To develop a transform that achieves lower mean squared error (MSE) than KLT in noisy scenarios.
  • To propose a practical, optimized transform for image compression.

Main Methods:

  • Developed a data model that includes quantization noise.
  • Formulated an optimal linear transform based on this model.

Related Experiment Videos

  • Proposed a modification of the Discrete Cosine Transform (DCT) based on the derived theory.

    • Main Results:

    • The proposed transform is non-orthogonal and yields a smaller MSE than KLT under quantization noise.
    • The transform's performance depends on signal statistics and bit-rate allocation per coefficient.
    • Coding experiments demonstrated a 0.2 dB Peak Signal-to-Noise Ratio (SNR) improvement over JPEG with minimal overhead.

    Conclusions:

    • Accounting for quantization noise leads to a more optimal linear transform than KLT.
    • The proposed DCT modification offers practical benefits for image compression.
    • This approach enhances compression efficiency without significant computational cost.