https://gitlab.com/wg1/jpeg-xl/-/blob/master/lib/jxl/opsin_p... has the numbers for sRGB to XYB.
Basically the M1 matrix for linear sRGB [linearR, linearG, linearB, 1] to approximate cone responses:
(I think in this normalization 1 means 250 nits, but not completely sure at this stage of optimizations -- we changed normalizations on this recently.)
M1 = [
[0.300, 0.622, 0.078, 0.0038],
[0.240, 0.682, 0.078, 0.0038],
[0.243, 0.205, 0.552, 0.0038],
[0, 0, 0, 1]
]
then non-linearity by cubic root, in decoding cube, see: https://gitlab.com/wg1/jpeg-xl/-/blob/master/lib/jxl/dec_xyb...
The LMS values after cubic root are coded by this matrix M2:
M2 = [[1, -1, 0], [1, 1, 0], [0, 0, 1]]
In practice Y->X and Y->B correlations are decorrelated, so M2 looks more like this:
M2 = [[1+a, -1+a, 0], [1, 1, 0], [b, b, 1]]
after decorrelations a is often around zero and b is around -0.5.
The first dimension in this formulation is X (red-green), second Y (luma), third B (blueness-yellowness).
For quantization, X, Y and B channels are multiplied by constants representing their psychovisual strength. X and B channels (the chromacity channels) are less important when quantization is low, and particularly X channel increases in strength when more quantization is done.
Cube is beautiful in the sense that it allows scaling the intensity without considerations, but it is quite awful in the near black psychovisual performance. That is why sRGB added a linear ramp, and I added biasing (homogeneous transform instead of 3x3).
Basically the M1 matrix for linear sRGB [linearR, linearG, linearB, 1] to approximate cone responses:
(I think in this normalization 1 means 250 nits, but not completely sure at this stage of optimizations -- we changed normalizations on this recently.)
M1 = [ [0.300, 0.622, 0.078, 0.0038], [0.240, 0.682, 0.078, 0.0038], [0.243, 0.205, 0.552, 0.0038], [0, 0, 0, 1] ]
then non-linearity by cubic root, in decoding cube, see: https://gitlab.com/wg1/jpeg-xl/-/blob/master/lib/jxl/dec_xyb...
The LMS values after cubic root are coded by this matrix M2:
M2 = [[1, -1, 0], [1, 1, 0], [0, 0, 1]]
In practice Y->X and Y->B correlations are decorrelated, so M2 looks more like this:
M2 = [[1+a, -1+a, 0], [1, 1, 0], [b, b, 1]]
after decorrelations a is often around zero and b is around -0.5.
The first dimension in this formulation is X (red-green), second Y (luma), third B (blueness-yellowness).
For quantization, X, Y and B channels are multiplied by constants representing their psychovisual strength. X and B channels (the chromacity channels) are less important when quantization is low, and particularly X channel increases in strength when more quantization is done.
Cube is beautiful in the sense that it allows scaling the intensity without considerations, but it is quite awful in the near black psychovisual performance. That is why sRGB added a linear ramp, and I added biasing (homogeneous transform instead of 3x3).