Generalize MovingMedianFilter to MovingPercentileFilter

* Make `percentile` configurable and rename class.
* Introduce convenience type `MovingMedianFilter` that
  maintains the behaviour of the old class with that name.
* Move home grown moving 95th percentile filter in
  `JitterEstimator` to this new utility class.

Bug: webrtc:14151
Change-Id: I17d525b6e0bc98c28568c7dfe94b72eeab4a1ca2
Reviewed-on: https://webrtc-review.googlesource.com/c/src/+/275311
Commit-Queue: Rasmus Brandt <brandtr@webrtc.org>
Reviewed-by: Åsa Persson <asapersson@webrtc.org>
Reviewed-by: Philip Eliasson <philipel@webrtc.org>
Reviewed-by: Mirko Bonadei <mbonadei@webrtc.org>
Cr-Commit-Position: refs/heads/main@{#38082}
This commit is contained in:
Rasmus Brandt 2022-09-14 16:22:24 +02:00 committed by WebRTC LUCI CQ
parent 64edb15e1e
commit 916648107b
6 changed files with 78 additions and 52 deletions

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@ -16,7 +16,7 @@
#include "absl/types/optional.h"
#include "api/units/time_delta.h"
#include "api/units/timestamp.h"
#include "rtc_base/numerics/moving_median_filter.h"
#include "rtc_base/numerics/moving_percentile_filter.h"
#include "system_wrappers/include/rtp_to_ntp_estimator.h"
namespace webrtc {

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@ -92,7 +92,9 @@ JitterEstimator::JitterEstimator(Clock* clock,
config_.frame_size_window.value_or(kDefaultFrameSizeWindow))),
max_frame_size_bytes_percentile_(
config_.max_frame_size_percentile.value_or(
kDefaultMaxFrameSizePercentile)),
kDefaultMaxFrameSizePercentile),
static_cast<size_t>(
config_.frame_size_window.value_or(kDefaultFrameSizeWindow))),
fps_counter_(30), // TODO(sprang): Use an estimator with limit based
// on time, rather than number of samples.
clock_(clock) {
@ -108,7 +110,6 @@ void JitterEstimator::Reset() {
var_frame_size_bytes2_ = 100;
avg_frame_size_median_bytes_.Reset();
max_frame_size_bytes_percentile_.Reset();
frame_sizes_in_percentile_filter_ = std::queue<int64_t>();
last_update_time_ = absl::nullopt;
prev_estimate_ = absl::nullopt;
prev_frame_size_ = absl::nullopt;
@ -168,14 +169,6 @@ void JitterEstimator::UpdateEstimate(TimeDelta frame_delay,
avg_frame_size_median_bytes_.Insert(frame_size.bytes());
}
if (config_.MaxFrameSizePercentileEnabled()) {
frame_sizes_in_percentile_filter_.push(frame_size.bytes());
if (frame_sizes_in_percentile_filter_.size() >
static_cast<size_t>(
config_.frame_size_window.value_or(kDefaultFrameSizeWindow))) {
max_frame_size_bytes_percentile_.Erase(
frame_sizes_in_percentile_filter_.front());
frame_sizes_in_percentile_filter_.pop();
}
max_frame_size_bytes_percentile_.Insert(frame_size.bytes());
}
@ -232,12 +225,16 @@ void JitterEstimator::UpdateEstimate(TimeDelta frame_delay,
// delayed. The next frame is of normal size (delta frame), and thus deltaFS
// will be << 0. This removes all frame samples which arrives after a key
// frame.
double max_frame_size_bytes = GetMaxFrameSizeEstimateBytes();
double filtered_max_frame_size_bytes =
config_.MaxFrameSizePercentileEnabled()
? max_frame_size_bytes_percentile_.GetFilteredValue()
: max_frame_size_bytes_;
if (delta_frame_bytes >
GetCongestionRejectionFactor() * max_frame_size_bytes) {
GetCongestionRejectionFactor() * filtered_max_frame_size_bytes) {
// Update the Kalman filter with the new data
kalman_filter_.PredictAndUpdate(frame_delay.ms(), delta_frame_bytes,
max_frame_size_bytes, var_noise_ms2_);
filtered_max_frame_size_bytes,
var_noise_ms2_);
}
} else {
double num_stddev = (delay_deviation_ms >= 0) ? num_stddev_delay_outlier
@ -268,16 +265,6 @@ JitterEstimator::Config JitterEstimator::GetConfigForTest() const {
return config_;
}
double JitterEstimator::GetMaxFrameSizeEstimateBytes() const {
if (config_.MaxFrameSizePercentileEnabled()) {
RTC_DCHECK_GT(frame_sizes_in_percentile_filter_.size(), 1u);
RTC_DCHECK_LE(frame_sizes_in_percentile_filter_.size(),
config_.frame_size_window.value_or(kDefaultFrameSizeWindow));
return max_frame_size_bytes_percentile_.GetPercentileValue();
}
return max_frame_size_bytes_;
}
double JitterEstimator::GetNumStddevDelayOutlier() const {
return config_.num_stddev_delay_outlier.value_or(kNumStdDevDelayOutlier);
}
@ -357,8 +344,12 @@ TimeDelta JitterEstimator::CalculateEstimate() {
config_.avg_frame_size_median
? avg_frame_size_median_bytes_.GetFilteredValue()
: avg_frame_size_bytes_;
double filtered_max_frame_size_bytes =
config_.MaxFrameSizePercentileEnabled()
? max_frame_size_bytes_percentile_.GetFilteredValue()
: max_frame_size_bytes_;
double worst_case_frame_size_deviation_bytes =
GetMaxFrameSizeEstimateBytes() - filtered_avg_frame_size_bytes;
filtered_max_frame_size_bytes - filtered_avg_frame_size_bytes;
double ret_ms = kalman_filter_.GetFrameDelayVariationEstimateSizeBased(
worst_case_frame_size_deviation_bytes) +
NoiseThreshold();

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@ -24,8 +24,7 @@
#include "modules/video_coding/timing/frame_delay_variation_kalman_filter.h"
#include "modules/video_coding/timing/rtt_filter.h"
#include "rtc_base/experiments/struct_parameters_parser.h"
#include "rtc_base/numerics/moving_median_filter.h"
#include "rtc_base/numerics/percentile_filter.h"
#include "rtc_base/numerics/moving_percentile_filter.h"
#include "rtc_base/rolling_accumulator.h"
namespace webrtc {
@ -133,7 +132,6 @@ class JitterEstimator {
private:
// These functions return values that could be overriden through the config.
double GetMaxFrameSizeEstimateBytes() const;
double GetNumStddevDelayOutlier() const;
double GetNumStddevSizeOutlier() const;
double GetCongestionRejectionFactor() const;
@ -176,10 +174,7 @@ class JitterEstimator {
double max_frame_size_bytes_;
// Percentile frame sized received (over a window). Only used if configured.
MovingMedianFilter<int64_t> avg_frame_size_median_bytes_;
// TODO(webrtc:14151): Make `MovingMedianFilter` take a percentile value and
// switch `max_frame_size_bytes_percentile_` over to that class.
PercentileFilter<int64_t> max_frame_size_bytes_percentile_;
std::queue<int64_t> frame_sizes_in_percentile_filter_;
MovingPercentileFilter<int64_t> max_frame_size_bytes_percentile_;
// TODO(bugs.webrtc.org/14381): Update `startup_frame_size_sum_bytes_` to
// DataSize when api/units have sufficient precision.
double startup_frame_size_sum_bytes_;

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@ -763,7 +763,7 @@ rtc_library("rtc_numerics") {
"numerics/math_utils.h",
"numerics/moving_average.cc",
"numerics/moving_average.h",
"numerics/moving_median_filter.h",
"numerics/moving_percentile_filter.h",
"numerics/percentile_filter.h",
"numerics/running_statistics.h",
"numerics/sequence_number_util.h",
@ -1682,7 +1682,7 @@ if (rtc_include_tests) {
"numerics/event_based_exponential_moving_average_unittest.cc",
"numerics/exp_filter_unittest.cc",
"numerics/moving_average_unittest.cc",
"numerics/moving_median_filter_unittest.cc",
"numerics/moving_percentile_filter_unittest.cc",
"numerics/percentile_filter_unittest.cc",
"numerics/running_statistics_unittest.cc",
"numerics/sequence_number_util_unittest.cc",

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@ -8,8 +8,8 @@
* be found in the AUTHORS file in the root of the source tree.
*/
#ifndef RTC_BASE_NUMERICS_MOVING_MEDIAN_FILTER_H_
#define RTC_BASE_NUMERICS_MOVING_MEDIAN_FILTER_H_
#ifndef RTC_BASE_NUMERICS_MOVING_PERCENTILE_FILTER_H_
#define RTC_BASE_NUMERICS_MOVING_PERCENTILE_FILTER_H_
#include <stddef.h>
@ -21,16 +21,18 @@
namespace webrtc {
// Class to efficiently get moving median filter from a stream of samples.
// Class to efficiently get moving percentile filter from a stream of samples.
template <typename T>
class MovingMedianFilter {
class MovingPercentileFilter {
public:
// Construct filter. `window_size` is how many latest samples are stored and
// used to take median. `window_size` must be positive.
explicit MovingMedianFilter(size_t window_size);
// Construct filter. `percentile` defines what percentile to track and
// `window_size` is how many latest samples are stored for finding the
// percentile. `percentile` must be between 0.0 and 1.0 (inclusive) and
// `window_size` must be greater than 0.
MovingPercentileFilter(float percentile, size_t window_size);
MovingMedianFilter(const MovingMedianFilter&) = delete;
MovingMedianFilter& operator=(const MovingMedianFilter&) = delete;
MovingPercentileFilter(const MovingPercentileFilter&) = delete;
MovingPercentileFilter& operator=(const MovingPercentileFilter&) = delete;
// Insert a new sample.
void Insert(const T& value);
@ -38,7 +40,7 @@ class MovingMedianFilter {
// Removes all samples;
void Reset();
// Get median over the latest window.
// Get percentile over the latest window.
T GetFilteredValue() const;
// The number of samples that are currently stored.
@ -51,14 +53,25 @@ class MovingMedianFilter {
const size_t window_size_;
};
// Convenience type for the common median case.
template <typename T>
MovingMedianFilter<T>::MovingMedianFilter(size_t window_size)
: percentile_filter_(0.5f), samples_stored_(0), window_size_(window_size) {
class MovingMedianFilter : public MovingPercentileFilter<T> {
public:
explicit MovingMedianFilter(size_t window_size)
: MovingPercentileFilter<T>(0.5f, window_size) {}
};
template <typename T>
MovingPercentileFilter<T>::MovingPercentileFilter(float percentile,
size_t window_size)
: percentile_filter_(percentile),
samples_stored_(0),
window_size_(window_size) {
RTC_CHECK_GT(window_size, 0);
}
template <typename T>
void MovingMedianFilter<T>::Insert(const T& value) {
void MovingPercentileFilter<T>::Insert(const T& value) {
percentile_filter_.Insert(value);
samples_.emplace_back(value);
++samples_stored_;
@ -70,21 +83,21 @@ void MovingMedianFilter<T>::Insert(const T& value) {
}
template <typename T>
T MovingMedianFilter<T>::GetFilteredValue() const {
T MovingPercentileFilter<T>::GetFilteredValue() const {
return percentile_filter_.GetPercentileValue();
}
template <typename T>
void MovingMedianFilter<T>::Reset() {
void MovingPercentileFilter<T>::Reset() {
percentile_filter_.Reset();
samples_.clear();
samples_stored_ = 0;
}
template <typename T>
size_t MovingMedianFilter<T>::GetNumberOfSamplesStored() const {
size_t MovingPercentileFilter<T>::GetNumberOfSamplesStored() const {
return samples_stored_;
}
} // namespace webrtc
#endif // RTC_BASE_NUMERICS_MOVING_MEDIAN_FILTER_H_
#endif // RTC_BASE_NUMERICS_MOVING_PERCENTILE_FILTER_H_

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@ -8,15 +8,42 @@
* be found in the AUTHORS file in the root of the source tree.
*/
#include "rtc_base/numerics/moving_median_filter.h"
#include "rtc_base/numerics/moving_percentile_filter.h"
#include <stdint.h>
#include <algorithm>
#include "test/gtest.h"
namespace webrtc {
// 25th percentile can be exactly found with a window of length 4.
TEST(MovingPercentileFilter, Percentile25ReturnsMovingPercentile25WithWindow4) {
MovingPercentileFilter<int> perc25(0.25f, 4);
const int64_t kSamples[10] = {1, 2, 3, 4, 4, 4, 5, 6, 7, 8};
const int64_t kExpectedFilteredValues[10] = {1, 1, 1, 1, 2, 3, 4, 4, 4, 5};
for (size_t i = 0; i < 10; ++i) {
perc25.Insert(kSamples[i]);
EXPECT_EQ(kExpectedFilteredValues[i], perc25.GetFilteredValue());
EXPECT_EQ(std::min<size_t>(i + 1, 4), perc25.GetNumberOfSamplesStored());
}
}
// 90th percentile becomes the 67th percentile with a window of length 4.
TEST(MovingPercentileFilter, Percentile90ReturnsMovingPercentile67WithWindow4) {
MovingPercentileFilter<int> perc67(0.67f, 4);
MovingPercentileFilter<int> perc90(0.9f, 4);
const int64_t kSamples[8] = {1, 10, 1, 9, 1, 10, 1, 8};
const int64_t kExpectedFilteredValues[9] = {1, 1, 1, 9, 9, 9, 9, 8};
for (size_t i = 0; i < 8; ++i) {
perc67.Insert(kSamples[i]);
perc90.Insert(kSamples[i]);
EXPECT_EQ(kExpectedFilteredValues[i], perc67.GetFilteredValue());
EXPECT_EQ(kExpectedFilteredValues[i], perc90.GetFilteredValue());
}
}
TEST(MovingMedianFilterTest, ProcessesNoSamples) {
MovingMedianFilter<int> filter(2);
EXPECT_EQ(0, filter.GetFilteredValue());