blob: e03e4fb04eb9ca85a891c580f6335a46be46e8f8 [file] [log] [blame]
 /* * Copyright (c) 2016 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/base/logging.h" #include "webrtc/base/timestampaligner.h" namespace rtc { TimestampAligner::TimestampAligner() : frames_seen_(0), offset_us_(0) {} TimestampAligner::~TimestampAligner() {} int64_t TimestampAligner::UpdateOffset(int64_t camera_time_us, int64_t system_time_us) { // Estimate the offset between system monotonic time and the capture // time from the camera. The camera is assumed to provide more // accurate timestamps than we get from the system time. But the // camera may use its own free-running clock with a large offset and // a small drift compared to the system clock. So the model is // basically // // y_k = c_0 + c_1 * x_k + v_k // // where x_k is the camera timestamp, believed to be accurate in its // own scale. y_k is our reading of the system clock. v_k is the // measurement noise, i.e., the delay from frame capture until the // system clock was read. // // It's possible to do (weighted) least-squares estimation of both // c_0 and c_1. Then we get the constants as c_1 = Cov(x,y) / // Var(x), and c_0 = mean(y) - c_1 * mean(x). Substituting this c_0, // we can rearrange the model as // // y_k = mean(y) + (x_k - mean(x)) + (c_1 - 1) * (x_k - mean(x)) + v_k // // Now if we use a weighted average which gradually forgets old // values, x_k - mean(x) is bounded, of the same order as the time // constant (and close to constant for a steady frame rate). In // addition, the frequency error |c_1 - 1| should be small. Cameras // with a frequency error up to 3000 ppm (3 ms drift per second) // have been observed, but frequency errors below 100 ppm could be // expected of any cheap crystal. // // Bottom line is that we ignore the c_1 term, and use only the estimator // // x_k + mean(y-x) // // where mean is plain averaging for initial samples, followed by // exponential averaging. // The input for averaging, y_k - x_k in the above notation. int64_t diff_us = system_time_us - camera_time_us; // The deviation from the current average. int64_t error_us = diff_us - offset_us_; // If the current difference is far from the currently estimated // offset, the filter is reset. This could happen, e.g., if the // camera clock is reset, or cameras are plugged in and out, or if // the application process is temporarily suspended. The limit of // 300 ms should make this unlikely in normal operation, and at the // same time, converging gradually rather than resetting the filter // should be tolerable for jumps in camera time below this // threshold. static const int64_t kResetLimitUs = 300000; if (std::abs(error_us) > kResetLimitUs) { LOG(LS_INFO) << "Resetting timestamp translation after averaging " << frames_seen_ << " frames. Old offset: " << offset_us_ << ", new offset: " << diff_us; frames_seen_ = 0; prev_translated_time_us_ = rtc::Optional(); } static const int kWindowSize = 100; if (frames_seen_ < kWindowSize) { ++frames_seen_; } offset_us_ += error_us / frames_seen_; return offset_us_; } int64_t TimestampAligner::ClipTimestamp(int64_t time_us, int64_t system_time_us) { // Make timestamps monotonic. if (!prev_translated_time_us_) { // Initialize. clip_bias_us_ = 0; } else if (time_us < *prev_translated_time_us_) { time_us = *prev_translated_time_us_; } // Clip to make sure we don't produce time stamps in the future. time_us -= clip_bias_us_; if (time_us > system_time_us) { clip_bias_us_ += time_us - system_time_us; time_us = system_time_us; } prev_translated_time_us_ = rtc::Optional(time_us); return time_us; } } // namespace rtc