[libcamera-devel] [PATCH v2] ipa: raspberrypi: alsc: Limit the calculated lambda values
Naushir Patuck
naush at raspberrypi.com
Tue Apr 5 08:57:58 CEST 2022
Under the right circumstances, the alsc calculations could spread the colour
errors across the entire image as lambda remains unbound. This would cause the
corrected image chroma values to slowly drift to incorrect values.
This change adds a config parameter (alsc.lambda_bound) that provides an upper
and lower bound to the lambda value at every stage of the calculation. With this
change, we now adjust the lambda values so that the average across the entire
grid is 1 instead of normalising to the minimum value.
Signed-off-by: Naushir Patuck <naush at raspberrypi.com>
Reviewed-by: David Plowman <david.plowman at raspberrypi.com>
---
src/ipa/raspberrypi/controller/rpi/alsc.cpp | 57 +++++++++++++++------
src/ipa/raspberrypi/controller/rpi/alsc.hpp | 1 +
2 files changed, 43 insertions(+), 15 deletions(-)
diff --git a/src/ipa/raspberrypi/controller/rpi/alsc.cpp b/src/ipa/raspberrypi/controller/rpi/alsc.cpp
index be3d1ae476cd..a88fee9f6d94 100644
--- a/src/ipa/raspberrypi/controller/rpi/alsc.cpp
+++ b/src/ipa/raspberrypi/controller/rpi/alsc.cpp
@@ -149,6 +149,7 @@ void Alsc::Read(boost::property_tree::ptree const ¶ms)
read_calibrations(config_.calibrations_Cb, params, "calibrations_Cb");
config_.default_ct = params.get<double>("default_ct", 4500.0);
config_.threshold = params.get<double>("threshold", 1e-3);
+ config_.lambda_bound = params.get<double>("lambda_bound", 0.05);
}
static double get_ct(Metadata *metadata, double default_ct);
@@ -610,30 +611,47 @@ static double compute_lambda_top_end(int i, double const M[XY][4],
// Gauss-Seidel iteration with over-relaxation.
static double gauss_seidel2_SOR(double const M[XY][4], double omega,
- double lambda[XY])
+ double lambda[XY], double lambda_bound)
{
+ const double min = 1 - lambda_bound, max = 1 + lambda_bound;
double old_lambda[XY];
int i;
for (i = 0; i < XY; i++)
old_lambda[i] = lambda[i];
lambda[0] = compute_lambda_bottom_start(0, M, lambda);
- for (i = 1; i < X; i++)
+ lambda[0] = std::clamp(lambda[0], min, max);
+ for (i = 1; i < X; i++) {
lambda[i] = compute_lambda_bottom(i, M, lambda);
- for (; i < XY - X; i++)
+ lambda[i] = std::clamp(lambda[i], min, max);
+ }
+ for (; i < XY - X; i++) {
lambda[i] = compute_lambda_interior(i, M, lambda);
- for (; i < XY - 1; i++)
+ lambda[i] = std::clamp(lambda[i], min, max);
+ }
+ for (; i < XY - 1; i++) {
lambda[i] = compute_lambda_top(i, M, lambda);
+ lambda[i] = std::clamp(lambda[i], min, max);
+ }
lambda[i] = compute_lambda_top_end(i, M, lambda);
+ lambda[i] = std::clamp(lambda[i], min, max);
// Also solve the system from bottom to top, to help spread the updates
// better.
lambda[i] = compute_lambda_top_end(i, M, lambda);
- for (i = XY - 2; i >= XY - X; i--)
+ lambda[i] = std::clamp(lambda[i], min, max);
+ for (i = XY - 2; i >= XY - X; i--) {
lambda[i] = compute_lambda_top(i, M, lambda);
- for (; i >= X; i--)
+ lambda[i] = std::clamp(lambda[i], min, max);
+ }
+ for (; i >= X; i--) {
lambda[i] = compute_lambda_interior(i, M, lambda);
- for (; i >= 1; i--)
+ lambda[i] = std::clamp(lambda[i], min, max);
+ }
+ for (; i >= 1; i--) {
lambda[i] = compute_lambda_bottom(i, M, lambda);
+ lambda[i] = std::clamp(lambda[i], min, max);
+ }
lambda[0] = compute_lambda_bottom_start(0, M, lambda);
+ lambda[0] = std::clamp(lambda[0], min, max);
double max_diff = 0;
for (i = 0; i < XY; i++) {
lambda[i] = old_lambda[i] + (lambda[i] - old_lambda[i]) * omega;
@@ -653,15 +671,26 @@ static void normalise(double *ptr, size_t n)
ptr[i] /= minval;
}
+// Rescale the values so that the avarage value is 1.
+static void reaverage(double *ptr, size_t n)
+{
+ double sum = 0;
+ for (size_t i = 0; i < n; i++)
+ sum += ptr[i];
+ double ratio = 1 / (sum / n);
+ for (size_t i = 0; i < n; i++)
+ ptr[i] *= ratio;
+}
+
static void run_matrix_iterations(double const C[XY], double lambda[XY],
double const W[XY][4], double omega,
- int n_iter, double threshold)
+ int n_iter, double threshold, double lambda_bound)
{
double M[XY][4];
construct_M(C, W, M);
double last_max_diff = std::numeric_limits<double>::max();
for (int i = 0; i < n_iter; i++) {
- double max_diff = fabs(gauss_seidel2_SOR(M, omega, lambda));
+ double max_diff = fabs(gauss_seidel2_SOR(M, omega, lambda, lambda_bound));
if (max_diff < threshold) {
LOG(RPiAlsc, Debug)
<< "Stop after " << i + 1 << " iterations";
@@ -675,10 +704,8 @@ static void run_matrix_iterations(double const C[XY], double lambda[XY],
<< last_max_diff << " to " << max_diff;
last_max_diff = max_diff;
}
- // We're going to normalise the lambdas so the smallest is 1. Not sure
- // this is really necessary as they get renormalised later, but I
- // suppose it does stop these quantities from wandering off...
- normalise(lambda, XY);
+ // We're going to normalise the lambdas so the total average is 1.
+ reaverage(lambda, XY);
}
static void add_luminance_rb(double result[XY], double const lambda[XY],
@@ -737,9 +764,9 @@ void Alsc::doAlsc()
compute_W(Cb, config_.sigma_Cb, Wb);
// Run Gauss-Seidel iterations over the resulting matrix, for R and B.
run_matrix_iterations(Cr, lambda_r_, Wr, config_.omega, config_.n_iter,
- config_.threshold);
+ config_.threshold, config_.lambda_bound);
run_matrix_iterations(Cb, lambda_b_, Wb, config_.omega, config_.n_iter,
- config_.threshold);
+ config_.threshold, config_.lambda_bound);
// Fold the calibrated gains into our final lambda values. (Note that on
// the next run, we re-start with the lambda values that don't have the
// calibration gains included.)
diff --git a/src/ipa/raspberrypi/controller/rpi/alsc.hpp b/src/ipa/raspberrypi/controller/rpi/alsc.hpp
index 9616b99ea7ca..d1dbe0d1d22d 100644
--- a/src/ipa/raspberrypi/controller/rpi/alsc.hpp
+++ b/src/ipa/raspberrypi/controller/rpi/alsc.hpp
@@ -41,6 +41,7 @@ struct AlscConfig {
std::vector<AlscCalibration> calibrations_Cb;
double default_ct; // colour temperature if no metadata found
double threshold; // iteration termination threshold
+ double lambda_bound; // upper/lower bound for lambda from a value of 1
};
class Alsc : public Algorithm
--
2.25.1
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