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Efficient Color Quantization Using Superpixels.

Mariusz Frackiewicz¹, Henryk Palus¹

¹Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.

Sensors (Basel, Switzerland)

|August 26, 2022

Summary

We developed superpixel image color quantization methods that reduce computation time and maintain image quality. While effective, they offer slightly lower image fidelity compared to traditional pixel-based techniques.

Keywords:

clustering color quantization computation rate image quality superpixel

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Area of Science:

Computer Vision
Image Processing
Color Theory

Background:

Traditional color quantization methods can be computationally intensive.
Superpixel segmentation offers a way to group similar image regions.
Efficient color quantization is crucial for various image processing applications.

Purpose of the Study:

To introduce novel color quantization methods utilizing superpixels.
To evaluate the performance of these superpixel-based methods.
To compare superpixel quantization against existing state-of-the-art techniques.

Main Methods:

Image segmentation into superpixels based on color similarity.
Quantization of superpixel colors using median cut, k-means, and fuzzy c-means.
Generation of color palettes and color mapping for quantized images.

Main Results:

Proposed superpixel methods significantly decrease computation time.
Quantized images maintain high visual quality.
A slight reduction in image quality was observed compared to pixel-based methods via multi-index evaluation.

Conclusions:

Superpixel color quantization offers a computationally efficient alternative.
The trade-off between speed and absolute image fidelity exists.
These methods are promising for applications requiring fast color quantization.