using DitherPunk
using DitherPunk: srgb2linear
using Images

Gradient Gallery

On color spaces

A simple linear gradient works well to reveal the characteristic patterns of different dithering algorithms.

srbg, linear = gradient_image(100, 800);
mosaicview(srbg, linear)

The pixel intensities in the image srgb increase linearly from 0 to 1. The second image linear has been converted from sRGB to a linear representation, which more closely matches our human perception of brightness.

The helper function test_on_gradient takes a dithering algorithm and runs it on both the srgb and the linear image. It then shows a comparison of both inputs and outputs:

function test_on_gradient(alg)
    srgb, linear = gradient_image(100, 800)
    dither_srgb = dither(srgb, alg)
    dither_linear = dither(linear, alg)

    return mosaicview([srgb, dither_srgb, linear, dither_linear]; ncol=1)
end;

Most dithering algorithms in DitherPunk.jl provide an optional parameter to_linear, which converts the input image to a linear color space before applying the dithering. Select what looks best!

Threshold dithering

`ConstantThreshold`

test_on_gradient(ConstantThreshold())

`WhiteNoiseThreshold`

test_on_gradient(WhiteNoiseThreshold())

Ordered dithering

Bayer matrices

`bayer_dithering`

test_on_gradient(Bayer())

The order of the Bayer-matrix can be specified through the parameter level, which defaults to 1. Level 2

test_on_gradient(Bayer(2))

Level 3

test_on_gradient(Bayer(3))

Level 4

test_on_gradient(Bayer(4))

Clustered / halftone dithering

The following methods have large characteristic patterns and are therefore better suited for large images.

`ClusteredDots`

test_on_gradient(ClusteredDots())

`CentralWhitePoint`

test_on_gradient(CentralWhitePoint())

`BalancedCenteredPoint`

test_on_gradient(BalancedCenteredPoint())

`Rhombus`

test_on_gradient(Rhombus())

ImageMagick methods

test_on_gradient(IM_checks())

test_on_gradient(IM_h4x4a())

test_on_gradient(IM_h6x6a())

test_on_gradient(IM_h8x8a())

test_on_gradient(IM_h4x4o())

test_on_gradient(IM_h6x6o())

test_on_gradient(IM_h8x8o())

test_on_gradient(IM_c5x5())

test_on_gradient(IM_c6x6())

test_on_gradient(IM_c7x7())

Error diffusion

Note

If you are a dithering expert, the following images might look unusual to you. This is because DitherPunk iterates over colums first due to Julia's column-first memory layout for arrays.

`SimpleErrorDiffusion`

test_on_gradient(SimpleErrorDiffusion())

`FloydSteinberg`

test_on_gradient(FloydSteinberg())

`JarvisJudice`

test_on_gradient(JarvisJudice())

`Stucki`

test_on_gradient(Stucki())

`Burkes`

test_on_gradient(Burkes())

`Sierra`

test_on_gradient(Sierra())

`TwoRowSierra`

test_on_gradient(TwoRowSierra())

`SierraLite`

test_on_gradient(SierraLite())

`Fan93()`

test_on_gradient(Fan93())

`ShiauFan()`

test_on_gradient(ShiauFan())

`ShiauFan2()`

test_on_gradient(ShiauFan2())

`Atkinson()`

test_on_gradient(Atkinson())

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