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/*
* Copyright (c) 2013 The WebRTC 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 in the root of the source
* tree. An additional intellectual property rights grant can be found
* in the file PATENTS. All contributing project authors may
* be found in the AUTHORS file in the root of the source tree.
*/
#include "webrtc/modules/remote_bitrate_estimator/overuse_estimator.h"
#include <assert.h>
#include <math.h>
#include <stdlib.h>
#include <string.h>
#include <algorithm>
#include "webrtc/base/logging.h"
#include "webrtc/modules/remote_bitrate_estimator/include/bwe_defines.h"
namespace webrtc {
enum { kMinFramePeriodHistoryLength = 60 };
enum { kDeltaCounterMax = 1000 };
OveruseEstimator::OveruseEstimator(const OverUseDetectorOptions& options)
: options_(options),
num_of_deltas_(0),
slope_(options_.initial_slope),
offset_(options_.initial_offset),
prev_offset_(options_.initial_offset),
E_(),
process_noise_(),
avg_noise_(options_.initial_avg_noise),
var_noise_(options_.initial_var_noise),
ts_delta_hist_() {
memcpy(E_, options_.initial_e, sizeof(E_));
memcpy(process_noise_, options_.initial_process_noise,
sizeof(process_noise_));
}
OveruseEstimator::~OveruseEstimator() {
ts_delta_hist_.clear();
}
void OveruseEstimator::Update(int64_t t_delta,
double ts_delta,
int size_delta,
BandwidthUsage current_hypothesis) {
const double min_frame_period = UpdateMinFramePeriod(ts_delta);
const double t_ts_delta = t_delta - ts_delta;
double fs_delta = size_delta;
++num_of_deltas_;
if (num_of_deltas_ > kDeltaCounterMax) {
num_of_deltas_ = kDeltaCounterMax;
}
// Update the Kalman filter.
E_[0][0] += process_noise_[0];
E_[1][1] += process_noise_[1];
if ((current_hypothesis == kBwOverusing && offset_ < prev_offset_) ||
(current_hypothesis == kBwUnderusing && offset_ > prev_offset_)) {
E_[1][1] += 10 * process_noise_[1];
}
const double h[2] = {fs_delta, 1.0};
const double Eh[2] = {E_[0][0]*h[0] + E_[0][1]*h[1],
E_[1][0]*h[0] + E_[1][1]*h[1]};
const double residual = t_ts_delta - slope_*h[0] - offset_;
const bool in_stable_state = (current_hypothesis == kBwNormal);
const double max_residual = 3.0 * sqrt(var_noise_);
// We try to filter out very late frames. For instance periodic key
// frames doesn't fit the Gaussian model well.
if (fabs(residual) < max_residual) {
UpdateNoiseEstimate(residual, min_frame_period, in_stable_state);
} else {
UpdateNoiseEstimate(residual < 0 ? -max_residual : max_residual,
min_frame_period, in_stable_state);
}
const double denom = var_noise_ + h[0]*Eh[0] + h[1]*Eh[1];
const double K[2] = {Eh[0] / denom,
Eh[1] / denom};
const double IKh[2][2] = {{1.0 - K[0]*h[0], -K[0]*h[1]},
{-K[1]*h[0], 1.0 - K[1]*h[1]}};
const double e00 = E_[0][0];
const double e01 = E_[0][1];
// Update state.
E_[0][0] = e00 * IKh[0][0] + E_[1][0] * IKh[0][1];
E_[0][1] = e01 * IKh[0][0] + E_[1][1] * IKh[0][1];
E_[1][0] = e00 * IKh[1][0] + E_[1][0] * IKh[1][1];
E_[1][1] = e01 * IKh[1][0] + E_[1][1] * IKh[1][1];
// The covariance matrix must be positive semi-definite.
bool positive_semi_definite = E_[0][0] + E_[1][1] >= 0 &&
E_[0][0] * E_[1][1] - E_[0][1] * E_[1][0] >= 0 && E_[0][0] >= 0;
assert(positive_semi_definite);
if (!positive_semi_definite) {
LOG(LS_ERROR) << "The over-use estimator's covariance matrix is no longer "
"semi-definite.";
}
slope_ = slope_ + K[0] * residual;
prev_offset_ = offset_;
offset_ = offset_ + K[1] * residual;
}
double OveruseEstimator::UpdateMinFramePeriod(double ts_delta) {
double min_frame_period = ts_delta;
if (ts_delta_hist_.size() >= kMinFramePeriodHistoryLength) {
ts_delta_hist_.pop_front();
}
std::list<double>::iterator it = ts_delta_hist_.begin();
for (; it != ts_delta_hist_.end(); it++) {
min_frame_period = std::min(*it, min_frame_period);
}
ts_delta_hist_.push_back(ts_delta);
return min_frame_period;
}
void OveruseEstimator::UpdateNoiseEstimate(double residual,
double ts_delta,
bool stable_state) {
if (!stable_state) {
return;
}
// Faster filter during startup to faster adapt to the jitter level
// of the network. |alpha| is tuned for 30 frames per second, but is scaled
// according to |ts_delta|.
double alpha = 0.01;
if (num_of_deltas_ > 10*30) {
alpha = 0.002;
}
// Only update the noise estimate if we're not over-using. |beta| is a
// function of alpha and the time delta since the previous update.
const double beta = pow(1 - alpha, ts_delta * 30.0 / 1000.0);
avg_noise_ = beta * avg_noise_
+ (1 - beta) * residual;
var_noise_ = beta * var_noise_
+ (1 - beta) * (avg_noise_ - residual) * (avg_noise_ - residual);
if (var_noise_ < 1) {
var_noise_ = 1;
}
}
} // namespace webrtc
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