gray op• Data kinds: image → image
• Call: fullseye.apply(img, "dither_ordered", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ dither_ordered: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ dither_ordered: knob b sweep (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ dither_ordered: other inputs (docs site)
*The 4th column is a colour (H,W,3) input. This op handles colour without crossing channels (it does not convolve the colour channel as a third spatial axis).*
Quantizes with ordered dithering (Bayer matrix). a is the bit count 1 to 8; b is the matrix size.
Before quantization, it adds a fixed amount determined by pixel position (a Bayer matrix; b sweeps 2x2 / 4x4 / 8x8) and then rounds. The error in flat regions is scattered pixel by pixel, so the steps turn into a fine checkerboard and the stripes become invisible. The error itself does not decrease — it only changes how things look, and the mean squared error is actually slightly larger than with _quantize_uniform.
Choosing between it and error diffusion (dither_floyd_steinberg): ordered dithering is independent per pixel, so tiling produces no seams, parallelisation is free, and the same input always returns the same output. Error diffusion looks smoother, but it depends on the scan order, so changing the crop position changes the result.
• gallery2d_gray_arith family guide
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.
dither_ordered 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_gray_arith — py -3.11 examples/gallery2d_gray_arith.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
gray)gamma · quantize_uniform · quantize_lloyd_max · quantization_error · dither_floyd_steinberg · companding_mu_law · banding_map · invert
*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.