// Copyright (c) the JPEG XL Project Authors. All rights reserved.

//

// Use of this source code is governed by a BSD-style

// license that can be found in the LICENSE file.

#include "lib/jxl/enc_noise.h"

#include <algorithm>

#include <cstdint>

#include <cstdlib>

#include <numeric>

#include <utility>

#include "lib/jxl/base/status.h"

#include "lib/jxl/enc_aux_out.h"

#include "lib/jxl/enc_optimize.h"

namespace jxl {

namespace {

using OptimizeArray = optimize::Array<double, NoiseParams::kNumNoisePoints>;

float GetScoreSumsOfAbsoluteDifferences(const Image3F& opsin, const int x,

                                        const int y, const int block_size) {

  const int small_bl_size_x = 3;

  const int small_bl_size_y = 4;

  const int kNumSAD =

      (block_size - small_bl_size_x) * (block_size - small_bl_size_y);

  // block_size x block_size reference pixels

  int counter = 0;

  const int offset = 2;

  std::vector<float> sad(kNumSAD, 0);

  for (int y_bl = 0; y_bl + small_bl_size_y < block_size; ++y_bl) {

    for (int x_bl = 0; x_bl + small_bl_size_x < block_size; ++x_bl) {

      float sad_sum = 0;

      // size of the center patch, we compare all the patches inside window with

      // the center one

      for (int cy = 0; cy < small_bl_size_y; ++cy) {

        for (int cx = 0; cx < small_bl_size_x; ++cx) {

          float wnd = 0.5f * (opsin.PlaneRow(1, y + y_bl + cy)[x + x_bl + cx] +

                              opsin.PlaneRow(0, y + y_bl + cy)[x + x_bl + cx]);

          float center =

              0.5f * (opsin.PlaneRow(1, y + offset + cy)[x + offset + cx] +

                      opsin.PlaneRow(0, y + offset + cy)[x + offset + cx]);

          sad_sum += std::abs(center - wnd);

        }

      }

      sad[counter++] = sad_sum;

    }

  }

  const int kSamples = (kNumSAD) / 2;

  // As with ROAD (rank order absolute distance), we keep the smallest half of

  // the values in SAD (we use here the more robust patch SAD instead of

  // absolute single-pixel differences).

  std::sort(sad.begin(), sad.end());

  const float total_sad_sum =

      std::accumulate(sad.begin(), sad.begin() + kSamples, 0.0f);

  return total_sad_sum / kSamples;

}

class NoiseHistogram {

 public:

  static constexpr int kBins = 256;

  NoiseHistogram() { std::fill(bins, bins + kBins, 0); }

  void Increment(const float x) { bins[Index(x)] += 1; }

  int Get(const float x) const { return bins[Index(x)]; }

  int Bin(const size_t bin) const { return bins[bin]; }

  int Mode() const {

    size_t max_idx = 0;

    for (size_t i = 0; i < kBins; i++) {

      if (bins[i] > bins[max_idx]) max_idx = i;

    }

    return max_idx;

  }

  double Quantile(double q01) const {

    const int64_t total = std::accumulate(bins, bins + kBins, int64_t{1});

    const int64_t target = static_cast<int64_t>(q01 * total);

    // Until sum >= target:

    int64_t sum = 0;

    size_t i = 0;

    for (; i < kBins; ++i) {

      sum += bins[i];

      // Exact match: assume middle of bin i

      if (sum == target) {

        return i + 0.5;

      }

      if (sum > target) break;

    }

    // Next non-empty bin (in case histogram is sparsely filled)

    size_t next = i + 1;

    while (next < kBins && bins[next] == 0) {

      ++next;

    }

    // Linear interpolation according to how far into next we went

    const double excess = target - sum;

    const double weight_next = bins[Index(next)] / excess;

    return ClampX(next * weight_next + i * (1.0 - weight_next));

  }

  // Inter-quartile range

  double IQR() const { return Quantile(0.75) - Quantile(0.25); }

 private:

  template <typename T>

  T ClampX(const T x) const {

    return std::min(std::max(static_cast<T>(0), x), static_cast<T>(kBins - 1));

  }

Unexecuted instantiation: enc_noise.cc:int jxl::(anonymous namespace)::NoiseHistogram::ClampX<int>(int) const

Unexecuted instantiation: enc_noise.cc:double jxl::(anonymous namespace)::NoiseHistogram::ClampX<double>(double) const

  size_t Index(const float x) const { return ClampX(static_cast<int>(x)); }

  uint32_t bins[kBins];

};

std::vector<float> GetSADScoresForPatches(const Image3F& opsin,

                                          const size_t block_s,

                                          const size_t num_bin,

                                          NoiseHistogram* sad_histogram) {

  std::vector<float> sad_scores(

      (opsin.ysize() / block_s) * (opsin.xsize() / block_s), 0.0f);

  int block_index = 0;

  for (size_t y = 0; y + block_s <= opsin.ysize(); y += block_s) {

    for (size_t x = 0; x + block_s <= opsin.xsize(); x += block_s) {

      float sad_sc = GetScoreSumsOfAbsoluteDifferences(opsin, x, y, block_s);

      sad_scores[block_index++] = sad_sc;

      sad_histogram->Increment(sad_sc * num_bin);

    }

  }

  return sad_scores;

}

float GetSADThreshold(const NoiseHistogram& histogram, const int num_bin) {

  // Here we assume that the most patches with similar SAD value is a "flat"

  // patches. However, some images might contain regular texture part and

  // generate second strong peak at the histogram

  // TODO(user) handle bimodal and heavy-tailed case

  const int mode = histogram.Mode();

  return static_cast<float>(mode) / NoiseHistogram::kBins;

}

// loss = sum asym * (F(x) - nl)^2 + kReg * num_points * sum (w[i] - w[i+1])^2

// where asym = 1 if F(x) < nl, kAsym if F(x) > nl.

struct LossFunction {

  explicit LossFunction(std::vector<NoiseLevel> nl0) : nl(std::move(nl0)) {}

  double Compute(const OptimizeArray& w, OptimizeArray* df,

                 bool skip_regularization = false) const {

    constexpr double kReg = 0.005;

    constexpr double kAsym = 1.1;

    double loss_function = 0;

    for (size_t i = 0; i < w.size(); i++) {

      (*df)[i] = 0;

    }

    for (auto ind : nl) {

      std::pair<int, float> pos = IndexAndFrac(ind.intensity);

      JXL_DASSERT(pos.first >= 0 && static_cast<size_t>(pos.first) <

                                        NoiseParams::kNumNoisePoints - 1);

      double low = w[pos.first];

      double hi = w[pos.first + 1];

      double val = low * (1.0f - pos.second) + hi * pos.second;

      double dist = val - ind.noise_level;

      if (dist > 0) {

        loss_function += kAsym * dist * dist;

        (*df)[pos.first] -= kAsym * (1.0f - pos.second) * dist;

        (*df)[pos.first + 1] -= kAsym * pos.second * dist;

      } else {

        loss_function += dist * dist;

        (*df)[pos.first] -= (1.0f - pos.second) * dist;

        (*df)[pos.first + 1] -= pos.second * dist;

      }

    }

    if (skip_regularization) return loss_function;

    for (size_t i = 0; i + 1 < w.size(); i++) {

      double diff = w[i] - w[i + 1];

      loss_function += kReg * nl.size() * diff * diff;

      (*df)[i] -= kReg * diff * nl.size();

      (*df)[i + 1] += kReg * diff * nl.size();

    }

    return loss_function;

  }

  std::vector<NoiseLevel> nl;

};

void OptimizeNoiseParameters(const std::vector<NoiseLevel>& noise_level,

                             NoiseParams* noise_params) {

  constexpr double kMaxError = 1e-3;

  static const double kPrecision = 1e-8;

  static const int kMaxIter = 40;

  float avg = 0;

  for (const NoiseLevel& nl : noise_level) {

    avg += nl.noise_level;

  }

  avg /= noise_level.size();

  LossFunction loss_function(noise_level);

  OptimizeArray parameter_vector;

  for (size_t i = 0; i < parameter_vector.size(); i++) {

    parameter_vector[i] = avg;

  }

  parameter_vector = optimize::OptimizeWithScaledConjugateGradientMethod(

      loss_function, parameter_vector, kPrecision, kMaxIter);

  OptimizeArray df = parameter_vector;

  float loss = loss_function.Compute(parameter_vector, &df,

                                     /*skip_regularization=*/true) /

               noise_level.size();

  // Approximation went too badly: escape with no noise at all.

  if (loss > kMaxError) {

    noise_params->Clear();

    return;

  }

  for (size_t i = 0; i < parameter_vector.size(); i++) {

    noise_params->lut[i] = std::max(parameter_vector[i], 0.0);

  }

}

std::vector<NoiseLevel> GetNoiseLevel(

    const Image3F& opsin, const std::vector<float>& texture_strength,

    const float threshold, const size_t block_s) {

  std::vector<NoiseLevel> noise_level_per_intensity;

  const int filt_size = 1;

  static const float kLaplFilter[filt_size * 2 + 1][filt_size * 2 + 1] = {

      {-0.25f, -1.0f, -0.25f},

      {-1.0f, 5.0f, -1.0f},

      {-0.25f, -1.0f, -0.25f},

  };

  // The noise model is built based on channel 0.5 * (X+Y) as we notice that it

  // is similar to the model 0.5 * (Y-X)

  size_t patch_index = 0;

  for (size_t y = 0; y + block_s <= opsin.ysize(); y += block_s) {

    for (size_t x = 0; x + block_s <= opsin.xsize(); x += block_s) {

      if (texture_strength[patch_index] <= threshold) {

        // Calculate mean value

        float mean_int = 0;

        for (size_t y_bl = 0; y_bl < block_s; ++y_bl) {

          for (size_t x_bl = 0; x_bl < block_s; ++x_bl) {

            mean_int += 0.5f * (opsin.PlaneRow(1, y + y_bl)[x + x_bl] +

                                opsin.PlaneRow(0, y + y_bl)[x + x_bl]);

          }

        }

        mean_int /= block_s * block_s;

        // Calculate Noise level

        float noise_level = 0;

        size_t count = 0;

        for (size_t y_bl = 0; y_bl < block_s; ++y_bl) {

          for (size_t x_bl = 0; x_bl < block_s; ++x_bl) {

            float filtered_value = 0;

            for (int y_f = -1 * filt_size; y_f <= filt_size; ++y_f) {

              if ((static_cast<ssize_t>(y_bl) + y_f) >= 0 &&

                  (y_bl + y_f) < block_s) {

                for (int x_f = -1 * filt_size; x_f <= filt_size; ++x_f) {

                  if ((static_cast<ssize_t>(x_bl) + x_f) >= 0 &&

                      (x_bl + x_f) < block_s) {

                    filtered_value +=

                        0.5f *

                        (opsin.PlaneRow(1, y + y_bl + y_f)[x + x_bl + x_f] +

                         opsin.PlaneRow(0, y + y_bl + y_f)[x + x_bl + x_f]) *

                        kLaplFilter[y_f + filt_size][x_f + filt_size];

                  } else {

                    filtered_value +=

                        0.5f *

                        (opsin.PlaneRow(1, y + y_bl + y_f)[x + x_bl - x_f] +

                         opsin.PlaneRow(0, y + y_bl + y_f)[x + x_bl - x_f]) *

                        kLaplFilter[y_f + filt_size][x_f + filt_size];

                  }

                }

              } else {

                for (int x_f = -1 * filt_size; x_f <= filt_size; ++x_f) {

                  if ((static_cast<ssize_t>(x_bl) + x_f) >= 0 &&

                      (x_bl + x_f) < block_s) {

                    filtered_value +=

                        0.5f *

                        (opsin.PlaneRow(1, y + y_bl - y_f)[x + x_bl + x_f] +

                         opsin.PlaneRow(0, y + y_bl - y_f)[x + x_bl + x_f]) *

                        kLaplFilter[y_f + filt_size][x_f + filt_size];

                  } else {

                    filtered_value +=

                        0.5f *

                        (opsin.PlaneRow(1, y + y_bl - y_f)[x + x_bl - x_f] +

                         opsin.PlaneRow(0, y + y_bl - y_f)[x + x_bl - x_f]) *

                        kLaplFilter[y_f + filt_size][x_f + filt_size];

                  }

                }

              }

            }

            noise_level += std::abs(filtered_value);

            ++count;

          }

        }

        noise_level /= count;

        NoiseLevel nl;

        nl.intensity = mean_int;

        nl.noise_level = noise_level;

        noise_level_per_intensity.push_back(nl);

      }

      ++patch_index;

    }

  }

  return noise_level_per_intensity;

}

Status EncodeFloatParam(float val, float precision, BitWriter* writer) {

  JXL_ENSURE(val >= 0);

  const int absval_quant = static_cast<int>(std::lround(val * precision));

  JXL_ENSURE(absval_quant < (1 << 10));

  writer->Write(10, absval_quant);

  return true;

}

}  // namespace

Status GetNoiseParameter(const Image3F& opsin, NoiseParams* noise_params,

                         float quality_coef) {

  // The size of a patch in decoder might be different from encoder's patch

  // size.

  // For encoder: the patch size should be big enough to estimate

  //              noise level, but, at the same time, it should be not too big

  //              to be able to estimate intensity value of the patch

  const size_t block_s = 8;

  const size_t kNumBin = 256;

  NoiseHistogram sad_histogram;

  std::vector<float> sad_scores =

      GetSADScoresForPatches(opsin, block_s, kNumBin, &sad_histogram);

  float sad_threshold = GetSADThreshold(sad_histogram, kNumBin);

  // If threshold is too large, the image has a strong pattern. This pattern

  // fools our model and it will add too much noise. Therefore, we do not add

  // noise for such images

  if (sad_threshold > 0.15f || sad_threshold <= 0.0f) {

    noise_params->Clear();

    return false;

  }

  std::vector<NoiseLevel> nl =

      GetNoiseLevel(opsin, sad_scores, sad_threshold, block_s);

  OptimizeNoiseParameters(nl, noise_params);

  for (float& i : noise_params->lut) {

    i *= quality_coef * 1.4;

  }

  return noise_params->HasAny();

}

Status EncodeNoise(const NoiseParams& noise_params, BitWriter* writer,

                   LayerType layer, AuxOut* aux_out) {

  JXL_ENSURE(noise_params.HasAny());

  return writer->WithMaxBits(

      NoiseParams::kNumNoisePoints * 16, layer, aux_out, [&]() -> Status {

        for (float i : noise_params.lut) {

          JXL_RETURN_IF_ERROR(EncodeFloatParam(i, kNoisePrecision, writer));

        }

        return true;

      });

}

}  // namespace jxl