5 ms·

Another rather substantial difference is in M2 matrix of Oklab. In my experiments I don't see S-participation in colors with high spatial frequency. Because in

Another rather substantial difference is in M2 matrix of Oklab. In my experiments I don't see S-participation in colors with high spatial frequency. Because in image compression a lot of information is in high spatial frequency, one gets favorable image compression when M2 matrix is without S contribution in Luma. We use just [b b 0, a -a 0, -0.5 * c, -0.5 * c, c] in the M2 phase in JPEG XL. The two 0s there don't bring S reception into Luma and redness-greenness observations. This difference can be because Oklab is based on XYZ which is based on 2 degree color samples. XYB is based on about 0.03 degree color samples. Perception seems to be different there -- to me it looks like S is not yet integrated into Luma experience at that resolution. In butteraugli color modeling is more complex: it is divided into high spatial frequency and low spatial frequency. S is brought only to the low spatial frequency color transforms. (Frequency separation there is by Laplacian pyramid.)

JyrkiAlakuijala 6y ago
One more interesting and substantial difference between Oklab and XYB is that XYB includes biases before the nonlinearity, i.e., one can consider the M1 matrix as a homogeneous matrix. These biases make the receptive model (more) linear close to zero, and the non-linearity ramps up when one goes further into high intensity. The idea there is to model the spontaneous opsin isomerization in the receptors. I believe sRGB approximated this by gluing a small linear ramp into the nonlinearity with if-then-logic.
- bjornornorn 6y ago
  Not that familiar with XYB and its properties. Is there anywhere I can read more? Found some specifications, but not anything on its properties. I think this might be a case where the requirements for image editing and image compression are different. For image editing, especially when working with HDR images, I think it is better to just have a simple power function, since this makes less assumptions on the exact viewing conditions. E.g a user might want to adjust exposure while editing an image, and if the prediction of hue changes when the exposure is altered, that would be confusing (which happen if more complex non-linearities are used). When compressing final images though, that wouldn’t be an issue in the same way.
  - jacobolus 6y ago
    I would consider just continuing to use the CIELAB adjusted cube root function, with a linear part near zero. It has been used widely for 45 years and people understand it pretty well. It is plenty fast to implement (just takes one extra conditional move or the like and one FMA).
    - JyrkiAlakuijala 6y ago
      We don't need to use something just because it is old. CIELAB is based on 2 degree samples of color. Colors work differently at smaller angles due to the different densities of receptors, particularly the larger size and lower density of S receptors. Pixels on most recent monitors are about 0.02 degrees, 100x smaller in angle, 10'000x smaller in area than what the old color research is based on.
      - deleted 6y ago
        [deleted]
  - JyrkiAlakuijala 6y ago
    https://gitlab.com/wg1/jpeg-xl/-/blob/master/lib/jxl/opsin_params.h 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-inl.h#L77 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).

JyrkiAlakuijala 6y ago

One more interesting and substantial difference between Oklab and XYB is that XYB includes biases before the nonlinearity, i.e., one can consider the M1 matrix as a homogeneous matrix. These biases make the receptive model (more) linear close to zero, and the non-linearity ramps up when one goes further into high intensity. The idea there is to model the spontaneous opsin isomerization in the receptors. I believe sRGB approximated this by gluing a small linear ramp into the nonlinearity with if-then-logic.

bjornornorn 6y ago
Not that familiar with XYB and its properties. Is there anywhere I can read more? Found some specifications, but not anything on its properties. I think this might be a case where the requirements for image editing and image compression are different. For image editing, especially when working with HDR images, I think it is better to just have a simple power function, since this makes less assumptions on the exact viewing conditions. E.g a user might want to adjust exposure while editing an image, and if the prediction of hue changes when the exposure is altered, that would be confusing (which happen if more complex non-linearities are used). When compressing final images though, that wouldn’t be an issue in the same way.
- jacobolus 6y ago
  I would consider just continuing to use the CIELAB adjusted cube root function, with a linear part near zero. It has been used widely for 45 years and people understand it pretty well. It is plenty fast to implement (just takes one extra conditional move or the like and one FMA).
  - JyrkiAlakuijala 6y ago
    We don't need to use something just because it is old. CIELAB is based on 2 degree samples of color. Colors work differently at smaller angles due to the different densities of receptors, particularly the larger size and lower density of S receptors. Pixels on most recent monitors are about 0.02 degrees, 100x smaller in angle, 10'000x smaller in area than what the old color research is based on.
    - deleted 6y ago
      [deleted]
- JyrkiAlakuijala 6y ago
  https://gitlab.com/wg1/jpeg-xl/-/blob/master/lib/jxl/opsin_params.h 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-inl.h#L77 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).