gray op• Data kinds: image → image
• Call: fullseye.apply(img, "banding_map", 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):
▸ banding_map: knob a sweep (docs site)
*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ banding_map: 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).*
Returns where tonal steps (banding) are visible. a is the bit count; b is the window.
A step becomes "visible" when 3 conditions hold together, and this op multiplies those 3 in turn.
1. It sits on the step grid — the value lies on the grid k*Δ. An image that has not been quantized has no banding (floating-point noise is dropped here).
2. The window moves by exactly 1 step — the difference between maximum and minimum in the window (peak-to-peak) is 1 times Δ. Neither 0 times (flat) nor 2 times or more (a real contour).
3. It is a sparse line — steps appear as thin lines along contour lines. If a wide area responds all at once, it is not a step but dithering or a fine pattern.
a is the quantization bit count 1-8 (if the input is already quantized, give its level count). b is the window size (3-9 pixels).
Why these 3 stages (measured): judging by peak-to-peak alone, white noise flagged 41.7 % of pixels and an ordered-dithered image 97.7 % as "steps" — because the local ptp merely happening to equal Δ satisfies the condition. Condition 1 drops the noise and condition 3 drops the dithering. On a gradient quantized to 3-bit, 7.0 % (the step lines themselves) remains.
Conditions of use: (1) If the input bit count is wrong it has no effect at all — when unsure, measure it first with effective_bit_depth. (2) If the input has ever been smoothly interpolated or resampled, condition 1 breaks (it leaves the grid). To measure steps, apply it to the image right after quantization.
• 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.
banding_map 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_ordered · dither_floyd_steinberg · companding_mu_law · 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.