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A modular neural network vector predictor for predictive image coding.

L C Wang1, S A Rizvi, N M Nasrabadi

  • 1SONY Semicond. Co. of America, San Jose, CA 95134, USA. lwang@ssa-de.sel.sony.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 16, 2008
PubMed
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This study introduces a modular neural network vector predictor to enhance predictive vector quantization (PVQ). The novel approach improves image prediction quality and achieves significant gains over standard multilayer perceptron (MLP) predictors.

Area of Science:

  • Digital Signal Processing
  • Machine Learning
  • Image Compression

Background:

  • Predictive Vector Quantization (PVQ) is crucial for efficient image compression.
  • Existing vector prediction methods, like Multilayer Perceptron (MLP) predictors, have limitations in adapting to diverse image content.
  • A need exists for more sophisticated prediction techniques to improve compression efficiency and perceptual quality.

Purpose of the Study:

  • To develop a modular neural network vector predictor that enhances the predictive component of PVQ schemes.
  • To optimize vector prediction by employing specialized predictors for different classes of input vectors.
  • To improve the overall performance and perceptual quality of image compression using PVQ.

Main Methods:

  • A modular neural network architecture comprising five expert predictors, each specialized for specific vector classes (e.g., stationary, horizontal, vertical, diagonal edges).

Related Experiment Videos

  • Input vectors are classified based on directional variances to select the most appropriate expert predictor.
  • An integrating unit combines expert outputs without transmitting side information, or with transmitted selection information for enhanced performance.

    • Main Results:

    • The proposed modular predictor achieves a 1.7 dB improvement over a single MLP predictor without transmitting predictor selection information.
    • An improvement of up to 3 dB is observed when predictor selection information is sent to the receiver.
    • Significant enhancements in the perceptual quality of predicted images were noted.

    Conclusions:

    • The modular neural network vector predictor offers a substantial improvement in PVQ performance compared to traditional single predictors.
    • The expert-based approach effectively adapts to different image vector characteristics, leading to better prediction accuracy.
    • This technique presents a promising direction for advancing image compression technologies through intelligent vector prediction.