Irregular image binning

Question

Is there a good way to "bin" image data, which is analogous to hardware binning of a detector array? I'd like it to treat image data such that multiple values are averaged to form one mega-sample, giving the impression of a coarser detector array.

Here's an try with ImageResize and ArrayResample:

Module[{data, fn},
 data = ImageData@ColorConvert[ExampleData[{"TestImage", "Lena"}], "Grayscale"];
 fn = ImageResize[#, Scaled[5], Resampling -> "Nearest"] &;
 fn@Image@ArrayResample[data, Scaled[0.2], Resampling -> "Nearest"]]

I would like to set the bins with more flexibility, for instance, binning in a single dimension.

Answer 1

binPixels[img_, binSize_] /;Length[binSize]==2 :=
   Module[{pieces, avg},
          pieces = ImagePartition[img, {{1, binSize[[1]]}, {1, binSize[[2]]}}];
          avg = Mean@Flatten@ImageData[#] &;
          ImageAssemble@Table[Image@Array[(avg@pieces[[i, j]]) &, Reverse@binSize],
                              {i, First@Dimensions[pieces]}, {j, Last@Dimensions[pieces]}]]

With[{img = ColorConvert[ExampleData[{"TestImage", "Lena"}], "Grayscale"]},
      binPixels[img, {5, 10}]]

ImageDimensions[%]
(* {515, 520} *)

Answer 2

ColorQuantize reduces the number of "colors" (in this case, of grey scale values), effectively binning pixels of the image.

ColorQuantize[ColorConvert[ExampleData[{"TestImage", "Lena"}], "Grayscale"], 3]

Use the Dithering->False option if you want the resulting image to not use dithering.

Answer 3

ImageAssemble@
 Map[ColorQuantize[#, 1] &, ImagePartition[img, {10, 10}], {2}]

to illustrate the increased colors, suppose we start with bill's quantized image:

qimg = ColorQuantize[
   ColorConvert[ExampleData[{"TestImage", "Lena"}], "Grayscale"], 3];
Union@Flatten@ImageData[qimg] // Length

3

then bin it..

binned = ImageAssemble@
   Map[ColorQuantize[#, 1] &, ImagePartition[qimg, {4, 4}], {2}];
Union@Flatten@ImageData[binned] // Length

95