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<p>In this paper, we propose <span>SoftBinReduce</span>, a data reduction method for Color Quantization. Our approach consists of four main steps: (1) identifying representatives in each channel, (2) generating three-dimensional bins, (3) distributing pixel values using a soft binning technique, and (4) repositioning the resulting bins. Additionally, we introduce an adapted Sort-Means algorithm for the Pixel Mapping phase, providing a strong initial guess. We conduct an extensive experimental evaluation primarily using the CQ100 dataset, but also the older Kodak and USC-SIPI datasets. We compare our method to two well-known data reduction techniques from the literature: pseudo-random and quasi-random sampling. The results demonstrate that our method outperforms both in terms of NMSE error versus the achieved speedup, particularly when the data is significantly reduced and with a higher number of colors in the generated palette. Moreover, an ablation study shows that all components of our method are necessary for producing high-quality results.</p>

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SoftBinReduce: data reduction for color quantization through soft binning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月16日

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  • Guillem Rodríguez-Corominas,
  • Maria J. Blesa,
  • Christian Blum

摘要

In this paper, we propose SoftBinReduce, a data reduction method for Color Quantization. Our approach consists of four main steps: (1) identifying representatives in each channel, (2) generating three-dimensional bins, (3) distributing pixel values using a soft binning technique, and (4) repositioning the resulting bins. Additionally, we introduce an adapted Sort-Means algorithm for the Pixel Mapping phase, providing a strong initial guess. We conduct an extensive experimental evaluation primarily using the CQ100 dataset, but also the older Kodak and USC-SIPI datasets. We compare our method to two well-known data reduction techniques from the literature: pseudo-random and quasi-random sampling. The results demonstrate that our method outperforms both in terms of NMSE error versus the achieved speedup, particularly when the data is significantly reduced and with a higher number of colors in the generated palette. Moreover, an ablation study shows that all components of our method are necessary for producing high-quality results.