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Texture Analysis for Enhanced Color Image Quantization

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Abstract:

A traditional problem with color image quantization techniques is their inability to handle smooth variations in intensity and chromaticity, leading to contours in the quantized image. To address this problem, this paper describes new techniques for augmenting the performance of a seminal color image quantization algorithm, the median-cut quantizer. Applying a simple texture analysis method from computer vision in conjunction with the median-cut algorithm using a new variant of a k-d tree, we show that contouring effects can be alleviated without resorting to dithering methods and the accompanying decrease in signal-to-noise ratio. The merits of this approach are evaluated using remotely sensed aerial imagery and synthetically generated scenes.

Document Type: Research Article

Affiliations: School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213-3891

Publication date: May 1, 1997

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