diff --git a/aom_dsp/noise_model.c b/aom_dsp/noise_model.c
index 702ff4d..de09cec 100644
--- a/aom_dsp/noise_model.c
+++ b/aom_dsp/noise_model.c
@@ -173,13 +173,17 @@
   return lut->points[lut->num_points - 1][1];
 }
 
+static double noise_strength_solver_get_bin_index(
+    const aom_noise_strength_solver_t *solver, double value) {
+  const double val =
+      fclamp(value, solver->min_intensity, solver->max_intensity);
+  const double range = solver->max_intensity - solver->min_intensity;
+  return (solver->num_bins - 1) * (val - solver->min_intensity) / range;
+}
+
 void aom_noise_strength_solver_add_measurement(
     aom_noise_strength_solver_t *solver, double block_mean, double noise_std) {
-  const double val =
-      AOMMIN(AOMMAX(block_mean, solver->min_intensity), solver->max_intensity);
-  const double range = solver->max_intensity - solver->min_intensity;
-  const double bin =
-      (solver->num_bins - 1) * (val - solver->min_intensity) / range;
+  const double bin = noise_strength_solver_get_bin_index(solver, block_mean);
   const int bin_i0 = (int)floor(bin);
   const int bin_i1 = AOMMIN(solver->num_bins - 1, bin_i0 + 1);
   const double a = bin - bin_i0;
@@ -256,70 +260,81 @@
   return ((double)i) / (n - 1) * range + solver->min_intensity;
 }
 
-int aom_noise_strength_solver_fit_piecewise(
-    const aom_noise_strength_solver_t *solver, aom_noise_strength_lut_t *lut) {
-  const double kTolerance = 0.1;
-  int low_point = 0;
-  aom_equation_system_t sys;
-  if (!equation_system_init(&sys, 2)) {
-    fprintf(stderr, "Failed to init equation system\n");
-    return 0;
+// Computes the residual if a point were to be removed from the lut. This is
+// calculated as the area between the output of the solver and the line segment
+// that would be formed between [x_{i - 1}, x_{i + 1}).
+static void update_piecewise_linear_residual(
+    const aom_noise_strength_solver_t *solver,
+    const aom_noise_strength_lut_t *lut, double *residual, int start, int end) {
+  const double dx =
+      (solver->max_intensity - solver->min_intensity) / solver->num_bins;
+  for (int i = AOMMAX(start, 1); i < AOMMIN(end, lut->num_points - 1); ++i) {
+    const int lower = AOMMAX(0, (int)floor(noise_strength_solver_get_bin_index(
+                                    solver, lut->points[i - 1][0])));
+    const int upper = AOMMIN(solver->num_bins - 1,
+                             (int)ceil(noise_strength_solver_get_bin_index(
+                                 solver, lut->points[i + 1][0])));
+    double r = 0;
+    for (int j = lower; j <= upper; ++j) {
+      const double x = aom_noise_strength_solver_get_center(solver, j);
+      if (x < lut->points[i - 1][0]) continue;
+      if (x >= lut->points[i + 1][0]) continue;
+      const double y = solver->eqns.x[j];
+      const double a = (x - lut->points[i - 1][0]) /
+                       (lut->points[i + 1][0] - lut->points[i - 1][0]);
+      const double estimate_y =
+          lut->points[i - 1][1] * (1.0 - a) + lut->points[i + 1][1] * a;
+      r += fabs(y - estimate_y);
+    }
+    residual[i] = r * dx;
   }
+}
 
-  if (!aom_noise_strength_lut_init(lut, solver->num_bins + 1)) {
+int aom_noise_strength_solver_fit_piecewise(
+    const aom_noise_strength_solver_t *solver, int max_output_points,
+    aom_noise_strength_lut_t *lut) {
+  const double kTolerance = 0.00625;
+  if (!aom_noise_strength_lut_init(lut, solver->num_bins)) {
     fprintf(stderr, "Failed to init lut\n");
     return 0;
   }
+  for (int i = 0; i < solver->num_bins; ++i) {
+    lut->points[i][0] = aom_noise_strength_solver_get_center(solver, i);
+    lut->points[i][1] = solver->eqns.x[i];
+  }
+  if (max_output_points < 0) {
+    max_output_points = solver->num_bins;
+  }
 
-  lut->points[0][0] = aom_noise_strength_solver_get_center(solver, 0);
-  lut->points[0][1] = solver->eqns.x[0];
-  lut->num_points = 1;
+  double *residual = aom_malloc(solver->num_bins * sizeof(*residual));
+  memset(residual, 0, sizeof(*residual) * solver->num_bins);
 
-  while (low_point < solver->num_bins - 1) {
-    int i = low_point;
-    equation_system_clear(&sys);
-    for (; i < solver->num_bins; ++i) {
-      int x = i - low_point;
-      double b = 1;
-      sys.A[0 * 2 + 0] += x * x;
-      sys.A[0 * 2 + 1] += x * b;
-      sys.A[1 * 2 + 0] += x * b;
-      sys.A[1 * 2 + 1] += b * b;
-      sys.b[0] += x * solver->eqns.x[i];
-      sys.b[1] += b * solver->eqns.x[i];
+  update_piecewise_linear_residual(solver, lut, residual, 0, solver->num_bins);
 
-      if (x > 1) {
-        double res = 0;
-        int k;
-        equation_system_solve(&sys);
-
-        for (k = low_point; k <= i; ++k) {
-          double y;
-          x = k - low_point;
-          y = sys.x[0] * x + sys.x[1];
-          y -= solver->eqns.x[k];
-          res += y * y;
-        }
-        const int n = i - low_point + 1;
-        if (sqrt(res / n) > kTolerance) {
-          low_point = i - 1;
-
-          lut->points[lut->num_points][0] =
-              aom_noise_strength_solver_get_center(solver, i - 1);
-          lut->points[lut->num_points][1] = solver->eqns.x[i - 1];
-          lut->num_points++;
-        }
+  // Greedily remove points if there are too many or if it doesn't hurt local
+  // approximation (never remove the end points)
+  while (lut->num_points > 2) {
+    int min_index = 1;
+    for (int j = 1; j < lut->num_points - 1; ++j) {
+      if (residual[j] < residual[min_index]) {
+        min_index = j;
       }
     }
-    if (i == solver->num_bins) {
-      lut->points[lut->num_points][0] =
-          aom_noise_strength_solver_get_center(solver, i - 1);
-      lut->points[lut->num_points][1] = solver->eqns.x[i - 1];
-      lut->num_points++;
+    double dx = lut->points[min_index + 1][0] - lut->points[min_index - 1][0];
+    double avg_residual = residual[min_index] / dx;
+    if (lut->num_points <= max_output_points && avg_residual > kTolerance) {
       break;
     }
+
+    const int num_remaining = lut->num_points - min_index - 1;
+    memmove(lut->points + min_index, lut->points + min_index + 1,
+            sizeof(lut->points[0]) * num_remaining);
+    lut->num_points--;
+
+    update_piecewise_linear_residual(solver, lut, residual, min_index - 1,
+                                     min_index + 1);
   }
-  equation_system_free(&sys);
+  aom_free(residual);
   return 1;
 }
 
@@ -847,3 +862,97 @@
   return y_model_different ? AOM_NOISE_STATUS_DIFFERENT_NOISE_TYPE
                            : AOM_NOISE_STATUS_OK;
 }
+
+int aom_noise_model_get_grain_parameters(aom_noise_model_t *const noise_model,
+                                         aom_film_grain_t *film_grain) {
+  if (noise_model->params.lag > 3) {
+    fprintf(stderr, "params.lag = %d > 3\n", noise_model->params.lag);
+    return 0;
+  }
+  memset(film_grain, 0, sizeof(*film_grain));
+  film_grain->ar_coeff_lag = noise_model->params.lag;
+
+  // Convert the scaling functions to 8 bit values
+  aom_noise_strength_lut_t scaling_points[3];
+  aom_noise_strength_solver_fit_piecewise(
+      &noise_model->combined_state[0].strength_solver, 14, scaling_points + 0);
+  aom_noise_strength_solver_fit_piecewise(
+      &noise_model->combined_state[1].strength_solver, 10, scaling_points + 1);
+  aom_noise_strength_solver_fit_piecewise(
+      &noise_model->combined_state[2].strength_solver, 10, scaling_points + 2);
+
+  double max_scaling_value = 1e-4;
+  for (int c = 0; c < 3; ++c) {
+    for (int i = 0; i < scaling_points[c].num_points; ++i) {
+      max_scaling_value =
+          AOMMAX(scaling_points[c].points[i][1], max_scaling_value);
+    }
+  }
+
+  // Scaling_shift values are in the range [8,11]
+  const int max_scaling_value_log2 =
+      clamp((int)floor(log2(max_scaling_value) + 1), 2, 5);
+  film_grain->scaling_shift = 5 + (8 - max_scaling_value_log2);
+  const double scale_factor = 1 << (8 - max_scaling_value_log2);
+  film_grain->num_y_points = scaling_points[0].num_points;
+  film_grain->num_cb_points = scaling_points[1].num_points;
+  film_grain->num_cr_points = scaling_points[2].num_points;
+
+  int(*film_grain_scaling[3])[2] = {
+    film_grain->scaling_points_y,
+    film_grain->scaling_points_cb,
+    film_grain->scaling_points_cr,
+  };
+  for (int c = 0; c < 3; c++) {
+    for (int i = 0; i < scaling_points[c].num_points; ++i) {
+      film_grain_scaling[c][i][0] = (int)(scaling_points[c].points[i][0] + 0.5);
+      film_grain_scaling[c][i][1] = clamp(
+          (int)(scale_factor * scaling_points[c].points[i][1] + 0.5), 0, 255);
+    }
+  }
+  aom_noise_strength_lut_free(scaling_points + 0);
+  aom_noise_strength_lut_free(scaling_points + 1);
+  aom_noise_strength_lut_free(scaling_points + 2);
+
+  // Convert the ar_coeffs into 8-bit values
+  double max_coeff = 1e-4, min_coeff = -1e-4;
+  for (int c = 0; c < 3; c++) {
+    aom_equation_system_t *eqns = &noise_model->combined_state[c].eqns;
+    for (int i = 0; i < eqns->n; ++i) {
+      max_coeff = AOMMAX(max_coeff, eqns->x[i]);
+      min_coeff = AOMMIN(min_coeff, eqns->x[i]);
+    }
+  }
+  // Shift value: AR coeffs range (values 6-9)
+  // 6: [-2, 2),  7: [-1, 1), 8: [-0.5, 0.5), 9: [-0.25, 0.25)
+  film_grain->ar_coeff_shift =
+      clamp(7 - (int)AOMMAX(1 + floor(log2(max_coeff)), ceil(log2(-min_coeff))),
+            6, 9);
+  double scale_ar_coeff = 1 << film_grain->ar_coeff_shift;
+  int *ar_coeffs[3] = {
+    film_grain->ar_coeffs_y,
+    film_grain->ar_coeffs_cb,
+    film_grain->ar_coeffs_cr,
+  };
+  for (int c = 0; c < 3; ++c) {
+    aom_equation_system_t *eqns = &noise_model->combined_state[c].eqns;
+    for (int i = 0; i < eqns->n; ++i) {
+      ar_coeffs[c][i] =
+          clamp((int)round(scale_ar_coeff * eqns->x[i]), -128, 127);
+    }
+  }
+
+  // At the moment, the noise modeling code assumes that the chroma scaling
+  // functions are a function of luma.
+  film_grain->cb_mult = 128;       // 8 bits
+  film_grain->cb_luma_mult = 192;  // 8 bits
+  film_grain->cb_offset = 256;     // 9 bits
+
+  film_grain->cr_mult = 128;       // 8 bits
+  film_grain->cr_luma_mult = 192;  // 8 bits
+  film_grain->cr_offset = 256;     // 9 bits
+
+  film_grain->chroma_scaling_from_luma = 0;
+  film_grain->grain_scale_shift = 0;
+  return 1;
+}
diff --git a/aom_dsp/noise_model.h b/aom_dsp/noise_model.h
index c373f45..b663268 100644
--- a/aom_dsp/noise_model.h
+++ b/aom_dsp/noise_model.h
@@ -17,6 +17,7 @@
 #endif  // __cplusplus
 
 #include <stdint.h>
+#include "aom_dsp/grain_synthesis.h"
 
 /*!\brief Wrapper of data required to represent linear system of eqns and soln.
  */
@@ -107,10 +108,12 @@
 
 /*!\brief Fits a reduced piecewise linear lut to the internal solution
  *
+ * \param[in] max_num_points  The maximum number of output points
  * \param[out] lut  The output piecewise linear lut.
  */
 int aom_noise_strength_solver_fit_piecewise(
-    const aom_noise_strength_solver_t *solver, aom_noise_strength_lut_t *lut);
+    const aom_noise_strength_solver_t *solver, int max_num_points,
+    aom_noise_strength_lut_t *lut);
 
 /*!\brief Helper for holding precomputed data for finding flat blocks.
  *
@@ -223,6 +226,16 @@
     const uint8_t *const denoised[3], int w, int h, int strides[3],
     int chroma_sub_log2[2], const uint8_t *const flat_blocks, int block_size);
 
+/*!\brief Converts the noise_model parameters to the corresponding
+ *    grain_parameters.
+ *
+ * The noise structs in this file are suitable for estimation (e.g., using
+ * floats), but the grain parameters in the bitstream are quantized. This
+ * function does the conversion by selecting the correct quantization levels.
+ */
+int aom_noise_model_get_grain_parameters(aom_noise_model_t *const noise_model,
+                                         aom_film_grain_t *film_grain);
+
 #ifdef __cplusplus
 }  // extern "C"
 #endif  // __cplusplus
diff --git a/test/noise_model_test.cc b/test/noise_model_test.cc
index ebbbd3e..992884a 100644
--- a/test/noise_model_test.cc
+++ b/test/noise_model_test.cc
@@ -1,3 +1,4 @@
+#include <math.h>
 #include <algorithm>
 #include <vector>
 
@@ -48,7 +49,7 @@
   }
 
   aom_noise_strength_lut_t lut;
-  EXPECT_EQ(1, aom_noise_strength_solver_fit_piecewise(&solver, &lut));
+  EXPECT_EQ(1, aom_noise_strength_solver_fit_piecewise(&solver, 2, &lut));
 
   ASSERT_EQ(2, lut.num_points);
   EXPECT_NEAR(0.0, lut.points[0][0], 1e-5);
@@ -60,6 +61,44 @@
   aom_noise_strength_solver_free(&solver);
 }
 
+TEST(NoiseStrengthSolver, SimplifiesCurve) {
+  const int num_bins = 256;
+  aom_noise_strength_solver_t solver;
+  EXPECT_EQ(1, aom_noise_strength_solver_init(&solver, num_bins));
+
+  // Create a parabolic input
+  for (int i = 0; i < 256; ++i) {
+    const double x = (i - 127.5) / 63.5;
+    aom_noise_strength_solver_add_measurement(&solver, i, x * x);
+  }
+  EXPECT_EQ(1, aom_noise_strength_solver_solve(&solver));
+
+  // First try to fit an unconstrained lut
+  aom_noise_strength_lut_t lut;
+  EXPECT_EQ(1, aom_noise_strength_solver_fit_piecewise(&solver, -1, &lut));
+  ASSERT_LE(20, lut.num_points);
+  aom_noise_strength_lut_free(&lut);
+
+  // Now constrain the maximum number of points
+  const int kMaxPoints = 9;
+  EXPECT_EQ(1,
+            aom_noise_strength_solver_fit_piecewise(&solver, kMaxPoints, &lut));
+  ASSERT_EQ(kMaxPoints, lut.num_points);
+
+  // Check that the input parabola is still well represented
+  EXPECT_NEAR(0.0, lut.points[0][0], 1e-5);
+  EXPECT_NEAR(4.0, lut.points[0][1], 0.1);
+  for (int i = 1; i < lut.num_points - 1; ++i) {
+    const double x = (lut.points[i][0] - 128.) / 64.;
+    EXPECT_NEAR(x * x, lut.points[i][1], 0.1);
+  }
+  EXPECT_NEAR(255.0, lut.points[kMaxPoints - 1][0], 1e-5);
+
+  EXPECT_NEAR(4.0, lut.points[kMaxPoints - 1][1], 0.1);
+  aom_noise_strength_lut_free(&lut);
+  aom_noise_strength_solver_free(&solver);
+}
+
 TEST(NoiseStrengthLut, LutEvalSinglePoint) {
   aom_noise_strength_lut_t lut;
   ASSERT_TRUE(aom_noise_strength_lut_init(&lut, 1));
@@ -407,7 +446,7 @@
 
   aom_noise_strength_lut_t lut;
   aom_noise_strength_solver_fit_piecewise(
-      &model_.latest_state[0].strength_solver, &lut);
+      &model_.latest_state[0].strength_solver, -1, &lut);
   ASSERT_EQ(2, lut.num_points);
   EXPECT_NEAR(0.0, lut.points[0][0], 1e-5);
   EXPECT_NEAR(1.0, lut.points[0][1], kStdEps);
@@ -475,7 +514,7 @@
   // one near kLowStd at 0, and the other near kHighStd and 255.
   aom_noise_strength_lut_t lut;
   aom_noise_strength_solver_fit_piecewise(
-      &model_.latest_state[0].strength_solver, &lut);
+      &model_.latest_state[0].strength_solver, 2, &lut);
   ASSERT_EQ(2, lut.num_points);
   EXPECT_NEAR(0, lut.points[0][0], 1e-4);
   EXPECT_NEAR(kLowStd, lut.points[0][1], kStdEps);
@@ -562,3 +601,200 @@
     }
   }
 }
+
+TEST(NoiseModelGetGrainParameters, TestLagSize) {
+  aom_film_grain_t film_grain;
+  for (int lag = 1; lag <= 3; ++lag) {
+    aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, lag };
+    aom_noise_model_t model;
+    EXPECT_TRUE(aom_noise_model_init(&model, params));
+    EXPECT_TRUE(aom_noise_model_get_grain_parameters(&model, &film_grain));
+    EXPECT_EQ(lag, film_grain.ar_coeff_lag);
+    aom_noise_model_free(&model);
+  }
+
+  aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, 4 };
+  aom_noise_model_t model;
+  EXPECT_TRUE(aom_noise_model_init(&model, params));
+  EXPECT_FALSE(aom_noise_model_get_grain_parameters(&model, &film_grain));
+  aom_noise_model_free(&model);
+}
+
+TEST(NoiseModelGetGrainParameters, TestARCoeffShiftBounds) {
+  struct TestCase {
+    double max_input_value;
+    int expected_ar_coeff_shift;
+    int expected_value;
+  };
+  const int lag = 1;
+  const int kNumTestCases = 19;
+  const TestCase test_cases[] = {
+    // Test cases for ar_coeff_shift = 9
+    { 0, 9, 0 },
+    { 0.125, 9, 64 },
+    { -0.125, 9, -64 },
+    { 0.2499, 9, 127 },
+    { -0.25, 9, -128 },
+    // Test cases for ar_coeff_shift = 8
+    { 0.25, 8, 64 },
+    { -0.2501, 8, -64 },
+    { 0.499, 8, 127 },
+    { -0.5, 8, -128 },
+    // Test cases for ar_coeff_shift = 7
+    { 0.5, 7, 64 },
+    { -0.5001, 7, -64 },
+    { 0.999, 7, 127 },
+    { -1, 7, -128 },
+    // Test cases for ar_coeff_shift = 6
+    { 1.0, 6, 64 },
+    { -1.0001, 6, -64 },
+    { 2.0, 6, 127 },
+    { -2.0, 6, -128 },
+    { 4, 6, 127 },
+    { -4, 6, -128 },
+  };
+  aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, lag };
+  aom_noise_model_t model;
+  EXPECT_TRUE(aom_noise_model_init(&model, params));
+
+  for (int i = 0; i < kNumTestCases; ++i) {
+    const TestCase &test_case = test_cases[i];
+    model.combined_state[0].eqns.x[0] = test_case.max_input_value;
+
+    aom_film_grain_t film_grain;
+    EXPECT_TRUE(aom_noise_model_get_grain_parameters(&model, &film_grain));
+    EXPECT_EQ(1, film_grain.ar_coeff_lag);
+    EXPECT_EQ(test_case.expected_ar_coeff_shift, film_grain.ar_coeff_shift);
+    EXPECT_EQ(test_case.expected_value, film_grain.ar_coeffs_y[0]);
+  }
+  aom_noise_model_free(&model);
+}
+
+TEST(NoiseModelGetGrainParameters, TestNoiseStrengthShiftBounds) {
+  struct TestCase {
+    double max_input_value;
+    int expected_scaling_shift;
+    int expected_value;
+  };
+  const int kNumTestCases = 10;
+  const TestCase test_cases[] = {
+    { 0, 11, 0 },      { 1, 11, 64 },     { 2, 11, 128 }, { 3.99, 11, 255 },
+    { 4, 10, 128 },    { 7.99, 10, 255 }, { 8, 9, 128 },  { 16, 8, 128 },
+    { 31.99, 8, 255 }, { 64, 8, 255 },  // clipped
+  };
+  const int lag = 1;
+  aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, lag };
+  aom_noise_model_t model;
+  EXPECT_TRUE(aom_noise_model_init(&model, params));
+
+  for (int i = 0; i < kNumTestCases; ++i) {
+    const TestCase &test_case = test_cases[i];
+    aom_equation_system_t &eqns = model.combined_state[0].strength_solver.eqns;
+    // Set the fitted scale parameters to be a constant value.
+    for (int j = 0; j < eqns.n; ++j) {
+      eqns.x[j] = test_case.max_input_value;
+    }
+    aom_film_grain_t film_grain;
+    EXPECT_TRUE(aom_noise_model_get_grain_parameters(&model, &film_grain));
+    // We expect a single constant segemnt
+    EXPECT_EQ(test_case.expected_scaling_shift, film_grain.scaling_shift);
+    EXPECT_EQ(test_case.expected_value, film_grain.scaling_points_y[0][1]);
+    EXPECT_EQ(test_case.expected_value, film_grain.scaling_points_y[1][1]);
+  }
+  aom_noise_model_free(&model);
+}
+
+// The AR coefficients are the same inputs used to generate "Test 2" in the test
+// vectors
+TEST(NoiseModelGetGrainParameters, GetGrainParametersReal) {
+  const double kInputCoeffsY[] = { 0.0315,  0.0073,  0.0218,  0.00235, 0.00511,
+                                   -0.0222, 0.0627,  -0.022,  0.05575, -0.1816,
+                                   0.0107,  -0.1966, 0.00065, -0.0809, 0.04934,
+                                   -0.1349, -0.0352, 0.41772, 0.27973, 0.04207,
+                                   -0.0429, -0.1372, 0.06193, 0.52032 };
+  const double kInputCoeffsCB[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,   0,
+                                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.25 };
+  const double kInputCoeffsCR[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  0,
+                                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5 };
+  const int kExpectedARCoeffsY[] = { 4,  1,   3,  0,   1,  -3,  8, -3,
+                                     7,  -23, 1,  -25, 0,  -10, 6, -17,
+                                     -5, 53,  36, 5,   -5, -18, 8, 67 };
+  const int kExpectedARCoeffsCB[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                                      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32 };
+  const int kExpectedARCoeffsCR[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                                      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 64 };
+  // Scaling function is initialized analytically with a sqrt function.
+  const int kNumScalingPointsY = 12;
+  const int kExpectedScalingPointsY[][2] = {
+    { 0, 0 },     { 13, 44 },   { 27, 62 },   { 40, 76 },
+    { 54, 88 },   { 67, 98 },   { 94, 117 },  { 121, 132 },
+    { 148, 146 }, { 174, 159 }, { 201, 171 }, { 255, 192 },
+  };
+
+  const int lag = 3;
+  aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, lag };
+  aom_noise_model_t model;
+  EXPECT_TRUE(aom_noise_model_init(&model, params));
+
+  // Setup the AR coeffs
+  memcpy(model.combined_state[0].eqns.x, kInputCoeffsY, sizeof(kInputCoeffsY));
+  memcpy(model.combined_state[1].eqns.x, kInputCoeffsCB,
+         sizeof(kInputCoeffsCB));
+  memcpy(model.combined_state[2].eqns.x, kInputCoeffsCR,
+         sizeof(kInputCoeffsCR));
+  for (int i = 0; i < model.combined_state[0].strength_solver.num_bins; ++i) {
+    const double x =
+        ((double)i) / (model.combined_state[0].strength_solver.num_bins - 1.0);
+    model.combined_state[0].strength_solver.eqns.x[i] = 6 * sqrt(x);
+    model.combined_state[1].strength_solver.eqns.x[i] = 2;
+    model.combined_state[2].strength_solver.eqns.x[i] = 4;
+  }
+
+  aom_film_grain_t film_grain;
+  EXPECT_TRUE(aom_noise_model_get_grain_parameters(&model, &film_grain));
+  EXPECT_EQ(lag, film_grain.ar_coeff_lag);
+  EXPECT_EQ(3, film_grain.ar_coeff_lag);
+  EXPECT_EQ(7, film_grain.ar_coeff_shift);
+  EXPECT_EQ(10, film_grain.scaling_shift);
+  EXPECT_EQ(kNumScalingPointsY, film_grain.num_y_points);
+
+  const int kNumARCoeffs = 24;
+  for (int i = 0; i < kNumARCoeffs; ++i) {
+    EXPECT_EQ(kExpectedARCoeffsY[i], film_grain.ar_coeffs_y[i]);
+  }
+  for (int i = 0; i < kNumARCoeffs + 1; ++i) {
+    EXPECT_EQ(kExpectedARCoeffsCB[i], film_grain.ar_coeffs_cb[i]);
+  }
+  for (int i = 0; i < kNumARCoeffs + 1; ++i) {
+    EXPECT_EQ(kExpectedARCoeffsCR[i], film_grain.ar_coeffs_cr[i]);
+  }
+  for (int i = 0; i < kNumScalingPointsY; ++i) {
+    EXPECT_EQ(kExpectedScalingPointsY[i][0], film_grain.scaling_points_y[i][0]);
+    EXPECT_EQ(kExpectedScalingPointsY[i][1], film_grain.scaling_points_y[i][1]);
+  }
+
+  // CB strength should just be a piecewise segment
+  EXPECT_EQ(2, film_grain.num_cb_points);
+  EXPECT_EQ(0, film_grain.scaling_points_cb[0][0]);
+  EXPECT_EQ(255, film_grain.scaling_points_cb[1][0]);
+  EXPECT_EQ(64, film_grain.scaling_points_cb[0][1]);
+  EXPECT_EQ(64, film_grain.scaling_points_cb[1][1]);
+
+  // CR strength should just be a piecewise segment
+  EXPECT_EQ(2, film_grain.num_cr_points);
+  EXPECT_EQ(0, film_grain.scaling_points_cr[0][0]);
+  EXPECT_EQ(255, film_grain.scaling_points_cr[1][0]);
+  EXPECT_EQ(128, film_grain.scaling_points_cr[0][1]);
+  EXPECT_EQ(128, film_grain.scaling_points_cr[1][1]);
+
+  EXPECT_EQ(128, film_grain.cb_mult);
+  EXPECT_EQ(192, film_grain.cb_luma_mult);
+  EXPECT_EQ(256, film_grain.cb_offset);
+  EXPECT_EQ(128, film_grain.cr_mult);
+  EXPECT_EQ(192, film_grain.cr_luma_mult);
+  EXPECT_EQ(256, film_grain.cr_offset);
+  EXPECT_EQ(0, film_grain.chroma_scaling_from_luma);
+  EXPECT_EQ(0, film_grain.grain_scale_shift);
+
+  aom_noise_model_free(&model);
+}