tor-browser

histogram.cc (5942B)

---

/*
*  Copyright (c) 2019 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 "modules/audio_coding/neteq/histogram.h"

#include <algorithm>
#include <cstdint>
#include <cstdlib>
#include <optional>

#include "rtc_base/checks.h"

namespace webrtc {

Histogram::Histogram(size_t num_buckets,
                    int forget_factor,
                    std::optional<double> start_forget_weight)
   : buckets_(num_buckets, 0),
     forget_factor_(0),
     base_forget_factor_(forget_factor),
     add_count_(0),
     start_forget_weight_(start_forget_weight) {
 RTC_DCHECK_LT(base_forget_factor_, 1 << 15);
}

Histogram::~Histogram() {}

// Each element in the vector is first multiplied by the forgetting factor
// `forget_factor_`. Then the vector element indicated by `iat_packets` is then
// increased (additive) by 1 - `forget_factor_`. This way, the probability of
// `value` is slightly increased, while the sum of the histogram remains
// constant (=1).
// Due to inaccuracies in the fixed-point arithmetic, the histogram may no
// longer sum up to 1 (in Q30) after the update. To correct this, a correction
// term is added or subtracted from the first element (or elements) of the
// vector.
// The forgetting factor `forget_factor_` is also updated. When the DelayManager
// is reset, the factor is set to 0 to facilitate rapid convergence in the
// beginning. With each update of the histogram, the factor is increased towards
// the steady-state value `base_forget_factor_`.
void Histogram::Add(int value) {
 RTC_DCHECK(value >= 0);
 RTC_DCHECK(value < static_cast<int>(buckets_.size()));
 int vector_sum = 0;  // Sum up the vector elements as they are processed.
 // Multiply each element in `buckets_` with `forget_factor_`.
 for (int& bucket : buckets_) {
   bucket = (static_cast<int64_t>(bucket) * forget_factor_) >> 15;
   vector_sum += bucket;
 }

 // Increase the probability for the currently observed inter-arrival time
 // by 1 - `forget_factor_`. The factor is in Q15, `buckets_` in Q30.
 // Thus, left-shift 15 steps to obtain result in Q30.
 buckets_[value] += (32768 - forget_factor_) << 15;
 vector_sum += (32768 - forget_factor_) << 15;  // Add to vector sum.

 // `buckets_` should sum up to 1 (in Q30), but it may not due to
 // fixed-point rounding errors.
 vector_sum -= 1 << 30;  // Should be zero. Compensate if not.
 if (vector_sum != 0) {
   // Modify a few values early in `buckets_`.
   int flip_sign = vector_sum > 0 ? -1 : 1;
   for (int& bucket : buckets_) {
     // Add/subtract 1/16 of the element, but not more than `vector_sum`.
     int correction = flip_sign * std::min(std::abs(vector_sum), bucket >> 4);
     bucket += correction;
     vector_sum += correction;
     if (std::abs(vector_sum) == 0) {
       break;
     }
   }
 }
 RTC_DCHECK(vector_sum == 0);  // Verify that the above is correct.

 ++add_count_;

 // Update `forget_factor_` (changes only during the first seconds after a
 // reset). The factor converges to `base_forget_factor_`.
 if (start_forget_weight_) {
   if (forget_factor_ != base_forget_factor_) {
     int old_forget_factor = forget_factor_;
     int forget_factor =
         (1 << 15) * (1 - start_forget_weight_.value() / (add_count_ + 1));
     forget_factor_ =
         std::max(0, std::min(base_forget_factor_, forget_factor));
     // The histogram is updated recursively by forgetting the old histogram
     // with `forget_factor_` and adding a new sample multiplied by |1 -
     // forget_factor_|. We need to make sure that the effective weight on the
     // new sample is no smaller than those on the old samples, i.e., to
     // satisfy the following DCHECK.
     RTC_DCHECK_GE((1 << 15) - forget_factor_,
                   ((1 << 15) - old_forget_factor) * forget_factor_ >> 15);
   }
 } else {
   forget_factor_ += (base_forget_factor_ - forget_factor_ + 3) >> 2;
 }
}

int Histogram::Quantile(int probability) {
 // Find the bucket for which the probability of observing an
 // inter-arrival time larger than or equal to `index` is larger than or
 // equal to `probability`. The sought probability is estimated using
 // the histogram as the reverse cumulant PDF, i.e., the sum of elements from
 // the end up until `index`. Now, since the sum of all elements is 1
 // (in Q30) by definition, and since the solution is often a low value for
 // `iat_index`, it is more efficient to start with `sum` = 1 and subtract
 // elements from the start of the histogram.
 int inverse_probability = (1 << 30) - probability;
 size_t index = 0;   // Start from the beginning of `buckets_`.
 int sum = 1 << 30;  // Assign to 1 in Q30.
 sum -= buckets_[index];

 while ((sum > inverse_probability) && (index < buckets_.size() - 1)) {
   // Subtract the probabilities one by one until the sum is no longer greater
   // than `inverse_probability`.
   ++index;
   sum -= buckets_[index];
 }
 return static_cast<int>(index);
}

// Set the histogram vector to an exponentially decaying distribution
// buckets_[i] = 0.5^(i+1), i = 0, 1, 2, ...
// buckets_ is in Q30.
void Histogram::Reset() {
 // Set temp_prob to (slightly more than) 1 in Q14. This ensures that the sum
 // of buckets_ is 1.
 uint16_t temp_prob = 0x4002;  // 16384 + 2 = 100000000000010 binary.
 for (int& bucket : buckets_) {
   temp_prob >>= 1;
   bucket = temp_prob << 16;
 }
 forget_factor_ = 0;  // Adapt the histogram faster for the first few packets.
 add_count_ = 0;
}

int Histogram::NumBuckets() const {
 return buckets_.size();
}

}  // namespace webrtc