Roll skia/third_party/skcms 289a2a7..86807d3 (1 commits)

https://skia.googlesource.com/skcms.git/+log/289a2a7..86807d3

2018-04-25 mtklein@chromium.org use linear segment instead of recalculating it


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Documentation for the AutoRoller is here:
https://skia.googlesource.com/buildbot/+/master/autoroll/README.md

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TBR=stani@google.com

Change-Id: Iba614caef0c1020f4e32752e96d85bcabb96c560
Reviewed-on: https://skia-review.googlesource.com/123644
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>

2 files changed, 57 insertions, 47 deletions

diff --git a/third_party/skcms/src/PolyTF.c b/third_party/skcms/src/PolyTF.c
index 451a4272b7..4b31b18d1f 100644
--- a/third_party/skcms/src/PolyTF.c
+++ b/third_party/skcms/src/PolyTF.c
@@ -66,7 +66,7 @@ static bool fit_poly_tf(const skcms_Curve* curve, skcms_PolyTF* tf) {
     }
 
     const int N = curve->table_entries == 0 ? 256
-                                            :(int)curve->table_entries;
+                                            : (int)curve->table_entries;
 
     // We'll test the quality of our fit by roundtripping through a skcms_TransferFunction,
     // either the inverse of the curve itself if it is parametric, or of its approximation if not.
@@ -77,64 +77,74 @@ static bool fit_poly_tf(const skcms_Curve* curve, skcms_PolyTF* tf) {
     } else if (!skcms_ApproximateCurve(curve, &baseline, &err)) {
         return false;
     }
+
+    // We'll borrow the linear section from baseline, which is either
+    // exactly correct, or already the approximation we'd use anyway.
+    tf->C = baseline.c;
+    tf->D = baseline.d;
+    if (baseline.f != 0) {
+        return false;  // Can't fit this (rare) kind of curve here.
+    }
+
+    // Detect linear baseline: (ax + b)^g + e --> ax ~~> Cx
+    if (baseline.g == 1 && baseline.d == 0 && baseline.b + baseline.e == 0) {
+        tf->A = 0;
+        tf->B = 0;
+        tf->C = baseline.a;
+        tf->D = INFINITY_;   // Always use Cx, never Ax^3+Bx^2+(1-A-B)
+        return true;
+    }
+    // This case is less likely, but also guards against divide by zero below.
+    if (tf->D == 1) {
+        tf->A = 0;
+        tf->B = 0;
+        return true;
+    }
+
+    // Number of points already fit in the linear section.
+    // If the curve isn't parametric and we approximated instead, this should be exact.
+    const int L = (int)(tf->D * (N-1)) + 1;
+
+    // TODO: handle special case of L == N-1 to avoid /0 in Gauss-Newton.
+
     skcms_TransferFunction inv;
     if (!skcms_TransferFunction_invert(&baseline, &inv)) {
         return false;
     }
 
-    const float kTolerances[] = { 1.5f / 65535.0f, 1.0f / 512.0f };
-    for (int t = 0; t < ARRAY_COUNT(kTolerances); t++) {
-        float f;
-        const int L = skcms_fit_linear(curve, N, kTolerances[t], &tf->C, &tf->D, &f);
-        if (f != 0) {
+    // Start with guess A = 0, i.e. f(x) ≈ x^2.
+    float P[4] = {0, 0,0,0};
+    for (int i = 0; i < 3; i++) {
+        if (!skcms_gauss_newton_step(skcms_eval_curve, curve,
+                                     eval_poly_tf, tf,
+                                     grad_poly_tf, tf,
+                                     P,
+                                     tf->D, 1, N-L)) {
             return false;
         }
+    }
 
-        if (tf->D == 1) {
-            tf->A = 0;
-            tf->B = 0;
-            return true;
-        }
+    float A = tf->A = P[0],
+          C = tf->C,
+          D = tf->D;
+    tf->B = (C*D - A*(D*D*D - 1) - 1) / (D*D - 1);
 
-        // Start with guess A = 0, i.e. f(x) = x^2, gamma = 2.
-        float P[4] = {0, 0,0,0};
-
-        for (int i = 0; i < 3; i++) {
-            if (!skcms_gauss_newton_step(skcms_eval_curve, curve,
-                                         eval_poly_tf, tf,
-                                         grad_poly_tf, tf,
-                                         P,
-                                         tf->D, 1, N-L)) {
-                goto NEXT;
-            }
-        }
+    for (int i = 0; i < N; i++) {
+        float x = i * (1.0f/(N-1));
 
-        float A = tf->A = P[0],
-              C = tf->C,
-              D = tf->D;
-        tf->B = (C*D - A*(D*D*D - 1) - 1) / (D*D - 1);
-
-        for (int i = 0; i < N; i++) {
-            float x = i * (1.0f/(N-1));
-
-            float rt = skcms_TransferFunction_eval(&inv, eval_poly_tf(x, tf, P));
-            if (!isfinitef_(rt)) {
-                goto NEXT;
-            }
-
-            const int tol = (i == 0 || i == N-1) ? 0
-                                                 : N/256;
-            int ix = (int)((N-1) * rt + 0.5f);
-            if (abs(i - ix) > tol) {
-                goto NEXT;
-            }
+        float rt = skcms_TransferFunction_eval(&inv, eval_poly_tf(x, tf, P));
+        if (!isfinitef_(rt)) {
+            return false;
         }
-        return true;
 
-    NEXT: ;
+        const int tol = (i == 0 || i == N-1) ? 0
+                                             : N/256;
+        int ix = (int)((N-1) * rt + 0.5f);
+        if (abs(i - ix) > tol) {
+            return false;
+        }
     }
-
-    return false;
+    return true;
 }
 
 void skcms_OptimizeForSpeed(skcms_ICCProfile* profile) {
diff --git a/third_party/skcms/version.sha1 b/third_party/skcms/version.sha1
index f4f6396aa8..59cb5c56da 100755
--- a/third_party/skcms/version.sha1
+++ b/third_party/skcms/version.sha1
@@ -1 +1 @@
-289a2a73aa2642075105c699bac996cb34c65661
\ No newline at end of file
+86807d30cc2951021e81dc4507a5c14b37aebd31
\ No newline at end of file