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Scan predictive vector quantization of multispectral images.

N D Memon¹, K Sayood

¹Dept. of Comput. Sci., Northern Illinois Univ., DeKalb, IL.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1996

Summary

This summary is machine-generated.

This study introduces a novel scan model to improve vector quantization (VQ) for image processing. The method reduces edge degradation, enhancing the quality of reconstructed images, especially for multispectral data.

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Area of Science:

Digital Image Processing
Data Compression
Computer Vision

Background:

Conventional vector quantization (VQ) partitions images into nonoverlapping blocks for quantization.
Edge regions in images generate high-frequency vectors, leading to significant degradation in reconstructed images.
Existing VQ methods struggle with accurately representing abrupt intensity variations, particularly in complex imagery.

Purpose of the Study:

To address the edge-degradation problem inherent in conventional vector quantization techniques.
To enhance the performance and visual quality of VQ, particularly for multispectral datasets.
To introduce an improved image partitioning strategy based on scan models.

Main Methods:

Developed a novel approach utilizing an appropriate scan model to partition images into vectors.
Reduced the generation of high-frequency vectors by optimizing the image partitioning process.
Applied the technique to multispectral datasets to evaluate its performance enhancement.

Main Results:

Significantly reduced visually annoying degradations caused by edge representation in VQ.
Demonstrated substantial improvements in the performance of VQ for multispectral data.
The proposed scan model effectively minimizes abrupt intensity variations within vectors.

Conclusions:

The scan model-based approach offers a superior alternative to standard VQ techniques for image compression.
This method effectively mitigates edge-related artifacts, leading to higher fidelity image reconstruction.
The technique shows particular promise for enhancing the compression of multispectral imagery.