Variable-Bit-Rate Video Frame-Size Prediction by the Extended Kalman Filter Using Levenberg-Marquardt Algorithm

It is crucial to dynamically predict the future frame-sizes (bit-rates) for multimedia networking. All of the conventional bit-rate predictors are based on the assumption that instantaneous bit-rates are known precisely all the time (in the absence of uncertainty) which is surely not realistic in pr...

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Vydané v:	IEEE transactions on broadcasting Ročník 69; číslo 1; s. 75 - 84
Hlavní autori:	Chang, Shih Yu, Wu, Hsiao-Chun, Yan, Kun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.03.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Covariance matrices expectation–maximization (EM) algorithm Extended Kalman filter Extended Kalman filter (EKF) Heuristic algorithms Kalman filters Levenberg–Marquardt algorithm (LMA) MPEG-4 Multimedia multimedia network Optimization Prediction algorithms Robustness (mathematics) Signal to noise ratio Streaming media Transform coding Uncertainty variable-bit-rate (VBR) video streaming Video compression
ISSN:	0018-9316, 1557-9611
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Abstract	It is crucial to dynamically predict the future frame-sizes (bit-rates) for multimedia networking. All of the conventional bit-rate predictors are based on the assumption that instantaneous bit-rates are known precisely all the time (in the absence of uncertainty) which is surely not realistic in practice. In this work, we propose a new expectation-maximization (EM) based extended Kalman filter (EKF) to predict the bit-rates, where the EKF state-transition models will be optimized by the Levenberg-Marquardt algorithm (LMA). The main advantages of our proposed novel EKF-based bit-rate prediction approach are given as follows. First, our proposed EKF-based predictor can optimally estimate the bit-rates in the presence of uncertainty and/or noise. Second, our proposed novel EKF-based bit-rate prediction approach does not require a separate classifier to determine the individual frame-types as the conventional approach so our approach would be more robust than the conventional approach. Numerical evaluation of bit-rate (frame-size) prediction is also conducted over three movies encoded by the MPEG-4 standard. Compared to the existing Kalman-filter based bit-rate prediction methods, our proposed new LMA-EKF predictor can achieve much better performance in terms of the normalized mean square error (NMSE) and the inverse of signal-to-noise-ratio (SNR).
AbstractList	It is crucial to dynamically predict the future frame-sizes (bit-rates) for multimedia networking. All of the conventional bit-rate predictors are based on the assumption that instantaneous bit-rates are known precisely all the time (in the absence of uncertainty) which is surely not realistic in practice. In this work, we propose a new expectation-maximization (EM) based extended Kalman filter (EKF) to predict the bit-rates, where the EKF state-transition models will be optimized by the Levenberg-Marquardt algorithm (LMA). The main advantages of our proposed novel EKF-based bit-rate prediction approach are given as follows. First, our proposed EKF-based predictor can optimally estimate the bit-rates in the presence of uncertainty and/or noise. Second, our proposed novel EKF-based bit-rate prediction approach does not require a separate classifier to determine the individual frame-types as the conventional approach so our approach would be more robust than the conventional approach. Numerical evaluation of bit-rate (frame-size) prediction is also conducted over three movies encoded by the MPEG-4 standard. Compared to the existing Kalman-filter based bit-rate prediction methods, our proposed new LMA-EKF predictor can achieve much better performance in terms of the normalized mean square error (NMSE) and the inverse of signal-to-noise-ratio (SNR).
Author	Wu, Hsiao-Chun Chang, Shih Yu Yan, Kun
Author_xml	– sequence: 1 givenname: Shih Yu orcidid: 0000-0002-3576-0021 surname: Chang fullname: Chang, Shih Yu email: shihyu.chang@sjsu.edu organization: Department of Applied Data Science, San Jose State University, San Jose, CA, USA – sequence: 2 givenname: Hsiao-Chun orcidid: 0000-0002-0178-1246 surname: Wu fullname: Wu, Hsiao-Chun email: wu@ece.lsu.edu organization: School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA, USA – sequence: 3 givenname: Kun orcidid: 0000-0002-2811-3758 surname: Yan fullname: Yan, Kun email: kyan5702@gmail.com organization: Department of Information and Telecommunication, Guangxi Key Laboratory of Wireless Wideband Communication, and Signal Processing, Guilin University of Electronic Technology, Guilin, Guangxi, China
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SubjectTerms	Algorithms Covariance matrices expectation–maximization (EM) algorithm Extended Kalman filter Extended Kalman filter (EKF) Heuristic algorithms Kalman filters Levenberg–Marquardt algorithm (LMA) MPEG-4 Multimedia multimedia network Optimization Prediction algorithms Robustness (mathematics) Signal to noise ratio Streaming media Transform coding Uncertainty variable-bit-rate (VBR) video streaming Video compression
Title	Variable-Bit-Rate Video Frame-Size Prediction by the Extended Kalman Filter Using Levenberg-Marquardt Algorithm
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