SoftBinReduce: data reduction for color quantization through soft binning

Abstract 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.