Recoloring starts from reading one region from the queue and recolor the current region and it's neighbours (if they haven't been colored). The process uses the shortest path to recolor all regions.
Here we see the effect of one palette using two different color distance methods.
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Left to right color distance methods:
CMC: based on the L*C*h color model. Takes RGB as input colors. CIE76: based on Lab color space. Takes RGB colors as input. (L2 norm on Lab parameters) CIE94: defined in the L*C*h* color space with differences in lightness, chroma and hue calculated from L*a*b* coordinates. CIE2000: use the same color space as CIE94 with corrections on perceptual uniformity issue. Euclidean: RGB colors. Here I put some images to compare : Quantized image: which colors assigned to each region based on closeness to the palette colors. Recolored by art Deco, and barn colors, where recoloring uses edge weights (color difference) to make decision. Recoloring using edge weights of the quantized image. Observation: color assignments seems doing a better job when palette color differences affected edge weights. Original region colors:
Left to right, showing the histogram of edge differences on original colors, quantized barn colors and quantized art Deco poster.
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