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authorGravatar skcms-skia-autoroll@skia-buildbots.google.com.iam.gserviceaccount.com <skcms-skia-autoroll@skia-buildbots.google.com.iam.gserviceaccount.com>2018-05-02 14:15:48 +0000
committerGravatar Skia Commit-Bot <skia-commit-bot@chromium.org>2018-05-02 14:43:47 +0000
commit83e6a67b694cdaa24b22e65b82d05bacd4b669fa (patch)
treefca871954c46d0e8d5f155ce948d9d76febdaa6a
parent20331dea3f6165ed5efa68ae826fdf2a0ab6642c (diff)
Roll skia/third_party/skcms a1c0fe6..33bdf05 (1 commits)
https://skia.googlesource.com/skcms.git/+log/a1c0fe6..33bdf05 2018-05-02 brianosman@google.com fit inverse parametric directly The AutoRoll server is located here: https://skcms-skia-roll.skia.org Documentation for the AutoRoller is here: https://skia.googlesource.com/buildbot/+/master/autoroll/README.md If the roll is causing failures, please contact the current sheriff, who should be CC'd on the roll, and stop the roller if necessary. CQ_INCLUDE_TRYBOTS=master.tryserver.blink:linux_trusty_blink_rel TBR=brianosman@google.com Change-Id: I4f26e49327795ca6eef1bf3dc76a3029be83531b Reviewed-on: https://skia-review.googlesource.com/125230 Reviewed-by: skcms-skia-autoroll <skcms-skia-autoroll@skia-buildbots.google.com.iam.gserviceaccount.com> Commit-Queue: skcms-skia-autoroll <skcms-skia-autoroll@skia-buildbots.google.com.iam.gserviceaccount.com>
-rw-r--r--third_party/skcms/src/TransferFunction.c89
-rwxr-xr-xthird_party/skcms/version.sha12
2 files changed, 58 insertions, 33 deletions
diff --git a/third_party/skcms/src/TransferFunction.c b/third_party/skcms/src/TransferFunction.c
index 14a7898f00..df728e1c7f 100644
--- a/third_party/skcms/src/TransferFunction.c
+++ b/third_party/skcms/src/TransferFunction.c
@@ -132,49 +132,57 @@ bool skcms_TransferFunction_invert(const skcms_TransferFunction* src, skcms_Tran
// Our overall strategy is then:
// For a couple tolerances,
// - skcms_fit_linear(): fit c,d,f iteratively to as many points as our tolerance allows
-// - fit_nonlinear(): fit g,a,b using Gauss-Newton given c,d,f (and by constraint, e)
+// - invert c,d,f
+// - fit_nonlinear(): fit g,a,b using Gauss-Newton given those inverted c,d,f
+// (and by constraint, inverted e) to the inverse of the table.
// Return the parameters with least maximum error.
//
-// To run Gauss-Newton to find g,a,b, we'll also need the gradient of the non-linear piece:
-// ∂tf/∂g = ln(ax + b)*(ax + b)^g
-// - ln(ad + b)*(ad + b)^g
-// ∂tf/∂a = xg(ax + b)^(g-1)
-// - dg(ad + b)^(g-1)
-// ∂tf/∂b = g(ax + b)^(g-1)
-// - g(ad + b)^(g-1)
+// To run Gauss-Newton to find g,a,b, we'll also need the gradient of the residuals
+// of round-trip f_inv(x), the inverse of the non-linear piece of f(x).
+//
+// let y = Table(x)
+// r(x) = x - f_inv(y)
+//
+// ∂r/∂g = ln(ay + b)*(ay + b)^g
+// - ln(ad + b)*(ad + b)^g
+// ∂r/∂a = yg(ay + b)^(g-1)
+// - dg(ad + b)^(g-1)
+// ∂r/∂b = g(ay + b)^(g-1)
+// - g(ad + b)^(g-1)
typedef struct {
const skcms_Curve* curve;
const skcms_TransferFunction* tf;
} rg_nonlinear_arg;
-// Return the residual of the skcms_Curve and skcms_TransferFunction with parameters P,
+// Return the residual of roundtripping skcms_Curve(x) through f_inv(y) with parameters P,
// and fill out the gradient of the residual into dfdP.
static float rg_nonlinear(float x, const void* ctx, const float P[3], float dfdP[3]) {
const rg_nonlinear_arg* arg = (const rg_nonlinear_arg*)ctx;
- const skcms_TransferFunction* tf = arg->tf;
+ const float y = skcms_eval_curve(x, arg->curve);
+ const skcms_TransferFunction* tf = arg->tf;
const float g = P[0], a = P[1], b = P[2],
c = tf->c, d = tf->d, f = tf->f;
- const float X = a*x+b,
- D = a*d+b;
- assert (X >= 0 && D >= 0);
+ const float Y = fmaxf_(a*y + b, 0.0f),
+ D = a*d + b;
+ assert (D >= 0);
// The gradient.
- dfdP[0] = 0.69314718f*log2f_(X)*powf_(X, g)
+ dfdP[0] = 0.69314718f*log2f_(Y)*powf_(Y, g)
- 0.69314718f*log2f_(D)*powf_(D, g);
- dfdP[1] = x*g*powf_(X, g-1)
+ dfdP[1] = y*g*powf_(Y, g-1)
- d*g*powf_(D, g-1);
- dfdP[2] = g*powf_(X, g-1)
+ dfdP[2] = g*powf_(Y, g-1)
- g*powf_(D, g-1);
// The residual.
- const float t = powf_(X, g)
- - powf_(D, g)
- + c*d + f;
- return skcms_eval_curve(x, arg->curve) - t;
+ const float f_inv = powf_(Y, g)
+ - powf_(D, g)
+ + c*d + f;
+ return x - f_inv;
}
int skcms_fit_linear(const skcms_Curve* curve, int N, float tol, float* c, float* d, float* f) {
@@ -281,7 +289,8 @@ bool skcms_ApproximateCurve(const skcms_Curve* curve,
*max_error = INFINITY_;
const float kTolerances[] = { 1.5f / 65535.0f, 1.0f / 512.0f };
for (int t = 0; t < ARRAY_COUNT(kTolerances); t++) {
- skcms_TransferFunction tf;
+ skcms_TransferFunction tf,
+ tf_inv;
int L = skcms_fit_linear(curve, N, kTolerances[t], &tf.c, &tf.d, &tf.f);
if (L == N) {
@@ -291,6 +300,11 @@ bool skcms_ApproximateCurve(const skcms_Curve* curve,
tf.a = tf.c;
tf.b = tf.f;
tf.c = tf.d = tf.e = tf.f = 0;
+
+ // We set tf directly, so calculate tf_inv to keep in sync.
+ if (!skcms_TransferFunction_invert(&tf, &tf_inv)) {
+ continue;
+ }
} else if (L == N - 1) {
// Degenerate case with only two points in the nonlinear segment. Solve directly.
tf.g = 1;
@@ -299,6 +313,11 @@ bool skcms_ApproximateCurve(const skcms_Curve* curve,
tf.b = skcms_eval_curve((N-2)*x_scale, curve)
- tf.a * (N-2)*x_scale;
tf.e = 0;
+
+ // We solved tf directly, so calculate tf_inv to keep in sync.
+ if (!skcms_TransferFunction_invert(&tf, &tf_inv)) {
+ continue;
+ }
} else {
// Start by guessing a gamma-only curve through the midpoint.
int mid = (L + N) / 2;
@@ -307,27 +326,33 @@ bool skcms_ApproximateCurve(const skcms_Curve* curve,
tf.g = log2f_(mid_y) / log2f_(mid_x);;
tf.a = 1;
tf.b = 0;
+ tf.e = tf.c*tf.d + tf.f
+ - powf_(tf.a*tf.d + tf.b, tf.g);
- if (!fit_nonlinear(curve, L,N, &tf)) {
+
+ if (!skcms_TransferFunction_invert(&tf, &tf_inv) ||
+ !fit_nonlinear(curve, L,N, &tf_inv)) {
continue;
}
- }
- // We want to calculate the error of this approximation now.
- // The most likely use case for this approximation is to be inverted and
- // used as the transfer function for a destination color space, so we'll
- // exercise that scenario here.
- skcms_TransferFunction inv;
- if (!skcms_TransferFunction_invert(&tf, &inv)) {
- // If we can't invert the transfer function, it's not of much practical use anyway.
- continue;
+ // We fit tf_inv, so calculate tf to keep in sync.
+ if (!skcms_TransferFunction_invert(&tf_inv, &tf)) {
+ continue;
+ }
}
+ // We find our error by roundtripping the table through tf_inv.
+ //
+ // (The most likely use case for this approximation is to be inverted and
+ // used as the transfer function for a destination color space.)
+ //
+ // We test using tf_inv, but all paths above have kept tf and tf_inv in sync.
+
float err = 0;
for (int i = 0; i < N; i++) {
float x = i * x_scale,
y = skcms_eval_curve(x, curve);
- err = fmaxf_(err, fabsf_(x - skcms_TransferFunction_eval(&inv, y)));
+ err = fmaxf_(err, fabsf_(x - skcms_TransferFunction_eval(&tf_inv, y)));
}
if (*max_error > err) {
*max_error = err;
diff --git a/third_party/skcms/version.sha1 b/third_party/skcms/version.sha1
index d8375810cf..9dd9012a37 100755
--- a/third_party/skcms/version.sha1
+++ b/third_party/skcms/version.sha1
@@ -1 +1 @@
-a1c0fe6a3700662aa31a6c66e26f66e42bd0700e \ No newline at end of file
+33bdf053cbce06980bc32d1caf2a02115825f0b0 \ No newline at end of file