
While working on the following more extensive blog post on color grading and while building a shadertoy in this context, I thought it would be nice to have a minimal version of Troy Sobotka's AgX [1] display transform that is easy to drop into any existing codebase - especially for testing purposes.

AgX's picture formation process works by applying the following steps:

- A matrix transformation on the input data (inset). The input is expected as linear tristimulus with Rec. 709 primary chromaticities ("linear sRGB")
- An encoding to log2 space followed by the application of a sigmoid curve
- An **optional** ASC-CDL-based look transform; I've included the default looks "Golden" and "Punchy"
- The inverse of the input matrix transform (outset) followed by the fitting EOTF (from *non-linear* Rec. 709)

The OCIO reference implementation of AgX uses a lookup table for the sigmoid curve, which is backed by a relatively complex tunable function. I replaced the lookup table and function with a simple polynomial approximation to limit the code footprint to a minimum.

The following graphic shows a comparison between the approximation and the reference.

![Comparison between the reference and the polynomial approximation.](https://media.missing-deadlines.com/iolite/blog/agx_minimal/agx_polynomial.svg)

Close enough, in my opinion, with a mean squared error of roughly *3.6705141e-06*. If you want to get even closer, I've added a 7th-order variant with an MSE of around *1.85907662e-06* to the appendix of this post.

You can grab the whole source of the implementation from the following shadertoy [2]:

:shadertoy{id="cd3XWr" t="0"}

The code is also available in the appendix of this post - easy to understand and integrate.

Enjoy!

## Appendix

### AgX Minimal Source

```glsl
// MIT License
//
// Copyright (c) 2024 Missing Deadlines (Benjamin Wrensch)
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE.

// All values used to derive this implementation are sourced from Troy’s initial AgX implementation/OCIO config file available here:
//   https://github.com/sobotka/AgX

// 0: Default, 1: Golden, 2: Punchy
#define AGX_LOOK 0

// Mean error^2: 3.6705141e-06
vec3 agxDefaultContrastApprox(vec3 x) {
  vec3 x2 = x * x;
  vec3 x4 = x2 * x2;
  
  return + 15.5     * x4 * x2
         - 40.14    * x4 * x
         + 31.96    * x4
         - 6.868    * x2 * x
         + 0.4298   * x2
         + 0.1191   * x
         - 0.00232;
}

vec3 agx(vec3 val) {
  const mat3 agx_mat = mat3(
    0.842479062253094, 0.0423282422610123, 0.0423756549057051,
    0.0784335999999992,  0.878468636469772,  0.0784336,
    0.0792237451477643, 0.0791661274605434, 0.879142973793104);
    
  const float min_ev = -12.47393f;
  const float max_ev = 4.026069f;

  // Input transform (inset)
  val = agx_mat * val;
  
  // Log2 space encoding
  val = clamp(log2(val), min_ev, max_ev);
  val = (val - min_ev) / (max_ev - min_ev);
  
  // Apply sigmoid function approximation
  val = agxDefaultContrastApprox(val);

  return val;
}

vec3 agxEotf(vec3 val) {
  const mat3 agx_mat_inv = mat3(
    1.19687900512017, -0.0528968517574562, -0.0529716355144438,
    -0.0980208811401368, 1.15190312990417, -0.0980434501171241,
    -0.0990297440797205, -0.0989611768448433, 1.15107367264116);
    
  // Inverse input transform (outset)
  val = agx_mat_inv * val;
  
  // sRGB IEC 61966-2-1 2.2 Exponent Reference EOTF Display
  // NOTE: We're linearizing the output here. Comment/adjust when
  // *not* using a sRGB render target
  val = pow(val, vec3(2.2));

  return val;
}

vec3 agxLook(vec3 val) {
  const vec3 lw = vec3(0.2126, 0.7152, 0.0722);
  float luma = dot(val, lw);
  
  // Default
  vec3 offset = vec3(0.0);
  vec3 slope = vec3(1.0);
  vec3 power = vec3(1.0);
  float sat = 1.0;
 
#if AGX_LOOK == 1
  // Golden
  slope = vec3(1.0, 0.9, 0.5);
  power = vec3(0.8);
  sat = 0.8;
#elif AGX_LOOK == 2
  // Punchy
  slope = vec3(1.0);
  power = vec3(1.35, 1.35, 1.35);
  sat = 1.4;
#endif
  
  // ASC CDL
  val = pow(val * slope + offset, power);
  return luma + sat * (val - luma);
}

// Sample usage
void main(/*...*/) {
  // ...
  value = agx(value);
  value = agxLook(value); // Optional
  value = agxEotf(value);
  // ...
}
```

### 7th Order Polynomial Approximation

```cpp
// Mean error^2: 1.85907662e-06
vec3 agxDefaultContrastApprox(vec3 x) {
  vec3 x2 = x * x;
  vec3 x4 = x2 * x2;
  vec3 x6 = x4 * x2;
  
  return - 17.86     * x6 * x
         + 78.01     * x6
         - 126.7     * x4 * x
         + 92.06     * x4
         - 28.72     * x2 * x
         + 4.361     * x2
         - 0.1718    * x
         + 0.002857;
}
```

## Further Reading

- [1] AgX by Troy Sobotka  
<https://github.com/sobotka/AgX>
- [2] AgX Minimal Shadertoy  
<https://www.shadertoy.com/view/cd3XWr>

## Changelog

- [2024.01.11] Added MIT license header and a note where the original values are sourced from
- [2023.09.09] Added info about the encoding of the input and output values


Contains a minimal implementation of Troy Sobotka's AgX display transform, which is easy to drop into any existing codebase - especially for testing purposes.

Minimal AgX Implementation

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