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Journal articles on the topic 'Estimation scalable de l'incertitude'

Author: Grafiati

Published: 25 May 2024

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1

Perret, Christian, P. Marchand, Arnaud Belleville, Rémy Garcon, Damien Sevrez, Stéphanie Poligot-Pitsch, Rachel Puechberty, and Gwen Glaziou. "La variabilité en fonction du temps des relations hauteur débit. Sa prise en compte dans l'estimation des incertitudes des données hydrométriques par une méthode tabulée." La Houille Blanche, no. 4 (August 2018): 65–72. http://dx.doi.org/10.1051/lhb/2018043.

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La démarche engagée depuis plusieurs années par la communauté des hydromètres pour quantifier les incertitudes associées au processus d'élaboration des données de débit mériterait de trouver des applications opérationnelles. La présente étude incite notamment les gestionnaires des stations hydrométriques à mieux valoriser les jaugeages effectués en systématisant la démarche de précision du modèle de courbe de tarage. Les auteurs proposent ensuite une démarche simplifiée de quantification de l'incertitude associée à une valeur de débit prédite par une courbe de tarage à partir du calcul de l'écart type des écarts en pour cent des jaugeages à la courbe de tarage et d'une tabulation en fonction des quantiles de débits observés. Elle s'appuie sur une démarche classique d'identification des sources d'incertitude et de leur propagation. Cette méthode permet de proposer une estimation de l'incertitude observée en moyenne sur les stations françaises pour la médiane des débits observés. On propose la formulation suivante : pour 45 à 55 % des stations françaises, la valeur la plus probable de l'incertitude au seuil de confiance de 95 % pour le quantile 50 % des débits observés en France est inférieure à 22 %.

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2

Jiang, Zhuqing, Likuo Wei, Ganmin Zeng, Shuwen Qi, Haiying Wang, Aidong Men, and Yun Zhou. "Bitrate Estimation for Spatial Scalable Videos." IEEE Transactions on Broadcasting 67, no. 2 (June 2021): 549–55. http://dx.doi.org/10.1109/tbc.2021.3064278.

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3

Wang, Xianglu. "Gaussian graphical model estimation with measurement error." JUSTC 53, no. 11 (2023): 1105. http://dx.doi.org/10.52396/justc-2022-0108.

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It is well known that regression methods designed for clean data will lead to erroneous results if directly applied to corrupted data. Despite the recent methodological and algorithmic advances in Gaussian graphical model estimation, how to achieve efficient and scalable estimation under contaminated covariates is unclear. Here a new methodology called convex conditioned innovative scalable efficient estimation (COCOISEE) for Gaussian graphical model under both additive and multiplicative measurement errors is developed. It combines the strengths of the innovative scalable efficient estimation in Gaussian graphical model and the nearest positive semi-definite matrix projection, thus enjoying stepwise convexity and scalability. Comprehensive theoretical guarantees are provided and the effectiveness of the proposed methodology is demonstrated through numerical studies.

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Cicala, Marco, Egidio D’Amato, Immacolata Notaro, and Massimiliano Mattei. "Scalable Distributed State Estimation in UTM Context." Sensors 20, no. 9 (May 8, 2020): 2682. http://dx.doi.org/10.3390/s20092682.

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This article proposes a novel approach to the Distributed State Estimation (DSE) problem for a set of co-operating UAVs equipped with heterogeneous on board sensors capable of exploiting certain characteristics typical of the UAS Traffic Management (UTM) context, such as high traffic density and the presence of limited range, Vehicle-to-Vehicle communication devices. The proposed algorithm is based on a scalable decentralized Kalman Filter derived from the Internodal Transformation Theory enhanced on the basis of the Consensus Theory. The general benefit of the proposed algorithm consists of, on the one hand, reducing the estimation problem to smaller local sub-problems, through a self-organization process of the local estimating nodes in response to the time varying communication topology; and on the other hand, of exploiting measures carried out nearby in order to improve the accuracy of the local estimates. In the UTM context, this enables each vehicle to estimate both its own position and velocity, as well as those of the neighboring vehicles, using both on board measurements and information transmitted by neighboring vehicles. A numerical simulation in a simplified UTM scenario is presented, in order to illustrate the salient aspects of the proposed algorithm.

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Li, Cheng, Sanvesh Srivastava, and David B. Dunson. "Simple, scalable and accurate posterior interval estimation." Biometrika 104, no. 3 (June 25, 2017): 665–80. http://dx.doi.org/10.1093/biomet/asx033.

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Summary Standard posterior sampling algorithms, such as Markov chain Monte Carlo procedures, face major challenges in scaling up to massive datasets. We propose a simple and general posterior interval estimation algorithm to rapidly and accurately estimate quantiles of the posterior distributions for one-dimensional functionals. Our algorithm runs Markov chain Monte Carlo in parallel for subsets of the data, and then averages quantiles estimated from each subset. We provide strong theoretical guarantees and show that the credible intervals from our algorithm asymptotically approximate those from the full posterior in the leading parametric order. Our algorithm has a better balance of accuracy and efficiency than its competitors across a variety of simulations and a real-data example.

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Emerson, Joseph, Robert Alicki, and Karol Życzkowski. "Scalable noise estimation with random unitary operators." Journal of Optics B: Quantum and Semiclassical Optics 7, no. 10 (September 21, 2005): S347—S352. http://dx.doi.org/10.1088/1464-4266/7/10/021.

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7

Chen, Dong, Hua You Su, Wen Mei, Li Xuan Wang, and Chun Yuan Zhang. "Scalable Parallel Motion Estimation on Muti-GPU System." Applied Mechanics and Materials 347-350 (August 2013): 3708–14. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3708.

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With NVIDIA’s parallel computing architecture CUDA, using GPU to speed up compute-intensive applications has become a research focus in recent years. In this paper, we proposed a scalable method for multi-GPU system to accelerate motion estimation algorithm, which is the most time consuming process in video encoding. Based on the analysis of data dependency and multi-GPU architecture, a parallel computing model and a communication model are designed. We tested our parallel algorithm and analyzed the performance with 10 standard video sequences in different resolutions using 4 NVIDIA GTX460 GPUs, and calculated the overall speedup. Our results show that a speedup of 36.1 times using 1 GPU and more than 120 times for 4 GPUs on 1920x1080 sequences. Further, our parallel algorithm demonstrated the potential of nearly linear speedup according to the number of GPUs in the system.

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8

Hassan, Beenish, Sobia Baig, and Saad Aslam. "On Scalability of FDD-Based Cell-Free Massive MIMO Framework." Sensors 23, no. 15 (August 7, 2023): 6991. http://dx.doi.org/10.3390/s23156991.

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Cell-free massive multiple-input multiple-output (MIMO) systems have the potential of providing joint services, including joint initial access, efficient clustering of access points (APs), and pilot allocation to user equipment (UEs) over large coverage areas with reduced interference. In cell-free massive MIMO, a large coverage area corresponds to the provision and maintenance of the scalable quality of service requirements for an infinitely large number of UEs. The research in cell-free massive MIMO is mostly focused on time division duplex mode due to the availability of channel reciprocity which aids in avoiding feedback overhead. However, the frequency division duplex (FDD) protocol still dominates the current wireless standards, and the provision of angle reciprocity aids in reducing this overhead. The challenge of providing a scalable cell-free massive MIMO system in an FDD setting is also prevalent, since computational complexity regarding signal processing tasks, such as channel estimation, precoding/combining, and power allocation, becomes prohibitively high with an increase in the number of UEs. In this work, we consider an FDD-based scalable cell-free network with angular reciprocity and a dynamic cooperation clustering approach. We have proposed scalability for our FDD cell-free and performed a comparative analysis with reference to channel estimation, power allocation, and precoding/combining techniques. We present expressions for scalable spectral efficiency, angle-based precoding/combining schemes and provide a comparison of overhead between conventional and scalable angle-based estimation as well as combining schemes. Simulations confirm that the proposed scalable cell-free network based on an FDD scheme outperforms the conventional matched filtering scheme based on scalable precoding/combining schemes. The angle-based LP-MMSE in the FDD cell-free network provides 14.3% improvement in spectral efficiency and 11.11% improvement in energy efficiency compared to the scalable MF scheme.

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9

Ju, Cheng, Susan Gruber, Samuel D. Lendle, Antoine Chambaz, Jessica M. Franklin, Richard Wyss, Sebastian Schneeweiss, and Mark J. van der Laan. "Scalable collaborative targeted learning for high-dimensional data." Statistical Methods in Medical Research 28, no. 2 (September 22, 2017): 532–54. http://dx.doi.org/10.1177/0962280217729845.

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Robust inference of a low-dimensional parameter in a large semi-parametric model relies on external estimators of infinite-dimensional features of the distribution of the data. Typically, only one of the latter is optimized for the sake of constructing a well-behaved estimator of the low-dimensional parameter of interest. Optimizing more than one of them for the sake of achieving a better bias-variance trade-off in the estimation of the parameter of interest is the core idea driving the general template of the collaborative targeted minimum loss-based estimation procedure. The original instantiation of the collaborative targeted minimum loss-based estimation template can be presented as a greedy forward stepwise collaborative targeted minimum loss-based estimation algorithm. It does not scale well when the number p of covariates increases drastically. This motivates the introduction of a novel instantiation of the collaborative targeted minimum loss-based estimation template where the covariates are pre-ordered. Its time complexity is [Formula: see text] as opposed to the original [Formula: see text], a remarkable gain. We propose two pre-ordering strategies and suggest a rule of thumb to develop other meaningful strategies. Because it is usually unclear a priori which pre-ordering strategy to choose, we also introduce another instantiation called SL-C-TMLE algorithm that enables the data-driven choice of the better pre-ordering strategy given the problem at hand. Its time complexity is [Formula: see text] as well. The computational burden and relative performance of these algorithms were compared in simulation studies involving fully synthetic data or partially synthetic data based on a real world large electronic health database; and in analyses of three real, large electronic health databases. In all analyses involving electronic health databases, the greedy collaborative targeted minimum loss-based estimation algorithm is unacceptably slow. Simulation studies seem to indicate that our scalable collaborative targeted minimum loss-based estimation and SL-C-TMLE algorithms work well. All C-TMLEs are publicly available in a Julia software package.

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10

ADACHI, Ryosuke, Yuh YAMASHITA, and Koichi KOBAYASHI. "Distributed Estimation over Delayed Sensor Network with Scalable Communication." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E102.A, no. 5 (May 1, 2019): 712–20. http://dx.doi.org/10.1587/transfun.e102.a.712.

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11

Chun-Su Park, Seung-Jin Baek, Seung-Won Jung, Hye-Soo Kim, and Sung-Jea Ko. "Estimation-Based Interlayer Intra Prediction for Scalable Video Coding." IEEE Transactions on Circuits and Systems for Video Technology 19, no. 12 (December 2009): 1902–7. http://dx.doi.org/10.1109/tcsvt.2009.2026945.

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12

Fröhlich, Fabian, Barbara Kaltenbacher, Fabian J. Theis, and Jan Hasenauer. "Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks." PLOS Computational Biology 13, no. 1 (January 23, 2017): e1005331. http://dx.doi.org/10.1371/journal.pcbi.1005331.

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13

Molino, A., F. Vacca, G. Masera, and T. Q. Nguyen. "Scalable phase extraction methods for phase plane motion estimation." IEE Proceedings - Vision, Image, and Signal Processing 153, no. 6 (2006): 860. http://dx.doi.org/10.1049/ip-vis:20060011.

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14

Choi, Jinha. "Motion vector memory reduction scheme for scalable motion estimation." Optical Engineering 48, no. 9 (September 1, 2009): 090502. http://dx.doi.org/10.1117/1.3212689.

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15

Jingning Han, Vinay Melkote, and Kenneth Rose. "An Estimation-Theoretic Framework for Spatially Scalable Video Coding." IEEE Transactions on Image Processing 23, no. 8 (August 2014): 3684–97. http://dx.doi.org/10.1109/tip.2014.2331761.

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16

Dimitrov, D., and E. Atanassov. "Scalable system with accelerators for financial option prices estimation." International Journal of Data Science 1, no. 4 (2016): 305. http://dx.doi.org/10.1504/ijds.2016.081367.

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17

Ghosal, S., and P. Vanek. "A fast scalable algorithm for discontinuous optical flow estimation." IEEE Transactions on Pattern Analysis and Machine Intelligence 18, no. 2 (1996): 181–94. http://dx.doi.org/10.1109/34.481542.

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18

Mietens, S., P. H. N. de With, and C. Hentschel. "Computational-complexity scalable motion estimation for mobile MPEG encoding." IEEE Transactions on Consumer Electronics 50, no. 1 (February 2004): 281–91. http://dx.doi.org/10.1109/tce.2004.1277875.

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19

Warrington, Stephen, Wai-Yip Chan, and Subramania Sudharsanan. "Scalable high-throughput variable block size motion estimation architecture." Microprocessors and Microsystems 33, no. 4 (June 2009): 319–25. http://dx.doi.org/10.1016/j.micpro.2009.02.011.

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20

Watts, Stephen C., Scott C. Ritchie, Michael Inouye, and Kathryn E. Holt. "FastSpar: rapid and scalable correlation estimation for compositional data." Bioinformatics 35, no. 6 (August 29, 2018): 1064–66. http://dx.doi.org/10.1093/bioinformatics/bty734.

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21

ABBAS, GHULAM. "Bandwidth Price Estimation for Scalable and Responsive Rate Control." Journal of Interconnection Networks 16, no. 03n04 (September 2016): 1650005. http://dx.doi.org/10.1142/s0219265916500055.

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This paper concerns the analysis of important algorithmic attributes, namely, the rate of convergence and scalability, and their impact on Network Utility Maximization (NUM). The contribution of the paper is a novel distributed rate control mechanism with strong convergence and scalability properties. The proposed algorithm employs a distinctive distributed framework, where rate control is derived as a Sequential Quadratic Programming (SQP) mechanism incorporated with interior-point and trust-region methods. The NUM problem is solved by a barrier method that penalizes any violation of constraints. Lagrangian is applied to the barrier objective function, where multipliers are estimated using Least-square method to iteratively solve the quadratic approximation of the Lagrangian function at the current point to generate a search direction. The uniqueness of the algorithm is that it allows sources to estimate bandwidth prices and thereby enforces a scalable network core by pushing algorithmic complexity to the edges. The fast convergence of the algorithm, in turn, improves the responsiveness of rate control and enables reduced buffer occupancy. The convergence of the proposed algorithm is proved theoretically and is evaluated via simulations. The results demonstrate reasonable reduction of computation-time in tracking the optimal rates and validate the strong convergence properties of the proposed algorithm.

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22

Chen, Yian, and Mihai Anitescu. "Scalable Physics-Based Maximum Likelihood Estimation Using Hierarchical Matrices." SIAM/ASA Journal on Uncertainty Quantification 11, no. 2 (June 5, 2023): 682–725. http://dx.doi.org/10.1137/21m1458880.

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23

Eschle, Jonas, Albert Navarro Puig, Rafael Silva Coutinho, and Nicola Serra. "zfit: scalable pythonic fitting." EPJ Web of Conferences 245 (2020): 06025. http://dx.doi.org/10.1051/epjconf/202024506025.

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Statistical modeling and fitting is a key element in most HEP analyses. This task is usually performed in the C++ based framework ROOT/RooFit. Recently the HEP community started shifting more to the Python language, which the tools above are only loose integrated into, and a lack of stable, native Python based toolkits became clear. We presented zfit, a project that aims at building a fitting ecosystem by providing a carefully designed, stable API and a workflow for libraries to communicate together with an implementation fully integrated into the Python ecosystem. It is built on top of one of the state-of-theart industry tools, TensorFlow, which is used the main computational backend. zfit provides data loading, extensive model building capabilities, loss creation, minimization and certain error estimation. Each part is also provided with convenient base classes built for customizability and extendability.

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24

Ila, Viorela, Lukas Polok, Marek Solony, and Pavel Svoboda. "SLAM++-A highly efficient and temporally scalable incremental SLAM framework." International Journal of Robotics Research 36, no. 2 (February 2017): 210–30. http://dx.doi.org/10.1177/0278364917691110.

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The most common way to deal with the uncertainty present in noisy sensorial perception and action is to model the problem with a probabilistic framework. Maximum likelihood estimation is a well-known estimation method used in many robotic and computer vision applications. Under Gaussian assumption, the maximum likelihood estimation converts to a nonlinear least squares problem. Efficient solutions to nonlinear least squares exist and they are based on iteratively solving sparse linear systems until convergence. In general, the existing solutions provide only an estimation of the mean state vector, the resulting covariance being computationally too expensive to recover. Nevertheless, in many simultaneous localization and mapping (SLAM) applications, knowing only the mean vector is not enough. Data association, obtaining reduced state representations, active decisions and next best view are only a few of the applications that require fast state covariance recovery. Furthermore, computer vision and robotic applications are in general performed online. In this case, the state is updated and recomputed every step and its size is continuously growing, therefore, the estimation process may become highly computationally demanding. This paper introduces a general framework for incremental maximum likelihood estimation called SLAM++, which fully benefits from the incremental nature of the online applications, and provides efficient estimation of both the mean and the covariance of the estimate. Based on that, we propose a strategy for maintaining a sparse and scalable state representation for large scale mapping, which uses information theory measures to integrate only informative and non-redundant contributions to the state representation. SLAM++ differs from existing implementations by performing all the matrix operations by blocks. This led to extremely fast matrix manipulation and arithmetic operations used in nonlinear least squares. Even though this paper tests SLAM++ efficiency on SLAM problems, its applicability remains general.

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25

Starr, Jeffrey, Scott Moses, and Le Gruenwald. "A scalable approach for estimation of resource availability using bitfields." Integrated Computer-Aided Engineering 11, no. 4 (October 26, 2004): 349–58. http://dx.doi.org/10.3233/ica-2004-11405.

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26

Cândido, Paulo Gustavo Lopes, Jonathan Andrade Silva, Elaine Ribeiro Faria, and Murilo Coelho Naldi. "Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation." Applied Sciences 12, no. 13 (June 25, 2022): 6464. http://dx.doi.org/10.3390/app12136464.

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The increasing volume and velocity of the continuously generated data (data stream) challenge machine learning algorithms, which must evolve to fit real-world problems. The data stream clustering algorithms face issues such as the rapidly increasing volume of the data, the variety of the number of clusters, and their shapes. The present work aims to improve the accuracy of sequential clustering batches of data streams for scenarios in which clusters evolve dynamically and continuously, automatically estimating their number. In order to achieve this goal, three evolutionary algorithms are presented, along with three novel algorithms designed to deal with clusters of normal distribution based on goodness-of-fit tests in the context of scalable batch stream clustering with automatic estimation of the number of clusters. All of them are developed on top of MapReduce, Discretized-Stream models, and the most recent MPC frameworks to provide scalability, reliability, resilience, and flexibility. The proposed algorithms are experimentally compared with state-of-the-art methods and present the best results for accuracy for normally distributed data sets, reaching their goal.

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27

Ullah, Mahbuba Sheba, Ahmed Tewfik, and Robert W. Heath. "Leveraging Waveform Structure to Develop a Power Scalable AoA Estimation." IEEE Open Journal of the Communications Society 2 (2021): 2739–59. http://dx.doi.org/10.1109/ojcoms.2021.3134812.

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28

Metta, Ravindra, Martin Becker, Prasad Bokil, Samarjit Chakraborty, and R. Venkatesh. "TIC: a scalable model checking based approach to WCET estimation." ACM SIGPLAN Notices 51, no. 5 (August 2016): 72–81. http://dx.doi.org/10.1145/2980930.2907961.

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29

Gong, Wei, Ivan Stojmenovic, Amiya Nayak, Kebin Liu, and Haoxiang Liu. "Fast and Scalable Counterfeits Estimation for Large-Scale RFID Systems." IEEE/ACM Transactions on Networking 24, no. 2 (April 2016): 1052–64. http://dx.doi.org/10.1109/tnet.2015.2406669.

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30

Bates, Stephen, and Robert Tibshirani. "Log‐ratio lasso: Scalable, sparse estimation for log‐ratio models." Biometrics 75, no. 2 (March 29, 2019): 613–24. http://dx.doi.org/10.1111/biom.12995.

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31

Zhang, Jingru, and Wei Lin. "Scalable estimation and regularization for the logistic normal multinomial model." Biometrics 75, no. 4 (April 29, 2019): 1098–108. http://dx.doi.org/10.1111/biom.13071.

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32

Mrak, M., N. Sprljan, and E. Izquierdo. "Motion estimation in temporal subbands for quality scalable motion coding." Electronics Letters 41, no. 19 (2005): 1050. http://dx.doi.org/10.1049/el:20052863.

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McLoughlin, Terence H., and Mark Campbell. "Scalable Sensing, Estimation, and Control Architecture for Large Spacecraft Formations." Journal of Guidance, Control, and Dynamics 30, no. 2 (March 2007): 289–300. http://dx.doi.org/10.2514/1.21322.

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Vincent, Robert J., David J. Ives, and Seb J. Savory. "Scalable Capacity Estimation for Nonlinear Elastic All-Optical Core Networks." Journal of Lightwave Technology 37, no. 21 (November 1, 2019): 5380–91. http://dx.doi.org/10.1109/jlt.2019.2942710.

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35

Kucuk, Kerem, and Adnan Kavak. "Scalable location estimation using smart antennas in wireless sensor networks." Ad Hoc Networks 8, no. 8 (November 2010): 889–903. http://dx.doi.org/10.1016/j.adhoc.2010.04.005.

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36

Fan, Yingying, and Jinchi Lv. "Innovated scalable efficient estimation in ultra-large Gaussian graphical models." Annals of Statistics 44, no. 5 (October 2016): 2098–126. http://dx.doi.org/10.1214/15-aos1416.

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37

Wang, Gang, Georgios B. Giannakis, and Jie Chen. "Robust and Scalable Power System State Estimation via Composite Optimization." IEEE Transactions on Smart Grid 10, no. 6 (November 2019): 6137–47. http://dx.doi.org/10.1109/tsg.2019.2897100.

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38

Jin, Ming, Igor Molybog, Reza Mohammadi-Ghazi, and Javad Lavaei. "Scalable and Robust State Estimation From Abundant But Untrusted Data." IEEE Transactions on Smart Grid 11, no. 3 (May 2020): 1880–94. http://dx.doi.org/10.1109/tsg.2019.2944986.

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Bijur, Gururaj, Ramakrishna Mundugar, Vinayak Mantoor, and Karunakar A Kotegar. "Estimation of Adaptation Parameters for Dynamic Video Adaptation in Wireless Network Using Experimental Method." Computers 10, no. 4 (March 24, 2021): 39. http://dx.doi.org/10.3390/computers10040039.

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A wireless network gives flexibility to the user in terms of mobility that attracts the user to use wireless communication more. The video communication in the wireless network experiences Quality of Services (QoS) and Quality of Experience (QoE) issues due to network dynamics. The parameters, such as node mobility, routing protocols, and distance between the nodes, play a major role in the quality of video communication. Scalable Video Coding (SVC) is an extension to H.264 Advanced Video Coding (AVC), allows partial removal of layers, and generates a valid adapted bit-stream. This adaptation feature enables the streaming of video data over a wireless network to meet the availability of the resources. The video adaptation is a dynamic process and requires prior knowledge to decide the adaptation parameter for extraction of the video levels. This research work aims at building the adaptation parameters that are required by the adaptation engines, such as Media Aware Network Elements (MANE), to perform adaptation on-the-fly. The prior knowledge improves the performances of the adaptation engines and gives the improved quality of the video communication. The unique feature of this work is that, here, we used an experimental evaluation method to identify the video levels that are suitable for a given network condition. In this paper, we estimated the adaptation parameters for streaming scalable video over the wireless network using the experimental method. The adaptation parameters are derived using node mobility, link bandwidth, and motion level of video sequences as deciding parameters. The experimentation is carried on the OMNeT++ tool, and Joint Scalable Video Module (JSVM) is used to encode and decode the scalable video data.

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40

Zheng, Liang-Wei. "Computation Controllable Mode Decision and Motion Estimation for Scalable Video Coding." ETRI Journal 35, no. 3 (June 1, 2013): 469–79. http://dx.doi.org/10.4218/etrij.13.0112.0421.

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41

HoangVan, Xiem. "Adaptive Quantization Parameter Estimation for HEVC Based Surveillance Scalable Video Coding." Electronics 9, no. 6 (May 30, 2020): 915. http://dx.doi.org/10.3390/electronics9060915.

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Visual surveillance systems have been playing a vital role in human modern life with a large number of applications, ranging from remote home management, public security to traffic monitoring. The recent High Efficiency Video Coding (HEVC) scalable extension, namely SHVC, provides not only the compression efficiency but also the adaptive streaming capability. However, SHVC is originally designed for videos captured from generic scenes rather than from visual surveillance systems. In this paper, we propose a novel HEVC based surveillance scalable video coding (SSVC) framework. First, to achieve high quality inter prediction, we propose a long-term reference coding method, which adaptively exploits the temporal correlation among frames in surveillance video. Second, to optimize the SSVC compression performance, we design a quantization parameter adaptation mechanism in which the relationship between SSVC rate-distortion (RD) performance and the quantization parameter is statistically modeled by a fourth-order polynomial function. Afterwards, an appropriate quantization parameter is derived for frames at long-term reference position. Experiments conducted for a common set of surveillance videos have shown that the proposed SSVC significantly outperforms the relevant SHVC standard, notably by around 6.9% and 12.6% bitrate saving for the low delay (LD) and random access (RA) coding configurations, respectively while still providing a similar perceptual decoded frame quality.

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Na, Sangkwon, and Chong-Min Kyung. "Activity-Based Motion Estimation Scheme for H.264 Scalable Video Coding." IEEE Transactions on Circuits and Systems for Video Technology 20, no. 11 (November 2010): 1475–85. http://dx.doi.org/10.1109/tcsvt.2010.2077493.

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43

Lin, Weiyao, Krit Panusopone, David M. Baylon, and Ming-Ting Sun. "A Computation Control Motion Estimation Method for Complexity-Scalable Video Coding." IEEE Transactions on Circuits and Systems for Video Technology 20, no. 11 (November 2010): 1533–43. http://dx.doi.org/10.1109/tcsvt.2010.2077773.

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44

Abebe, Ameha Tsegaye, and Chung G. Kang. "Joint Channel Estimation and MUD for Scalable Grant-Free Random Access." IEEE Communications Letters 23, no. 12 (December 2019): 2229–33. http://dx.doi.org/10.1109/lcomm.2019.2945577.

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Chiu, Man-Yau, and Wan-Chi Siu. "Computationally-scalable motion estimation algorithm for H.264/AVC video coding." IEEE Transactions on Consumer Electronics 56, no. 2 (May 2010): 895–903. http://dx.doi.org/10.1109/tce.2010.5506017.

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Lai, Yeong-Kang, Tian-En Hsieh, and Lien-Fei Chen. "Scalable Motion Estimation Processor Core for Multimedia System-on-Chip Applications." Japanese Journal of Applied Physics 46, no. 4B (April 24, 2007): 2238–43. http://dx.doi.org/10.1143/jjap.46.2238.

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van Hinsbergen, Chris P. I. J., Thomas Schreiter, Frank S. Zuurbier, J. W. C. van Lint, and Henk J. van Zuylen. "Localized Extended Kalman Filter for Scalable Real-Time Traffic State Estimation." IEEE Transactions on Intelligent Transportation Systems 13, no. 1 (March 2012): 385–94. http://dx.doi.org/10.1109/tits.2011.2175728.

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Arjona, Laura, Hugo Landaluce, Asier Perallos, and Enrique Onieva. "Scalable RFID Tag Estimator With Enhanced Accuracy and Low Estimation Time." IEEE Signal Processing Letters 24, no. 7 (July 2017): 982–86. http://dx.doi.org/10.1109/lsp.2017.2692201.

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Fujita, Kohei, Tsuyoshi Ichimura, Muneo Hori, Lalith Maddegedara, and Seizo Tanaka. "Scalable Multicase Urban Earthquake Simulation Method for Stochastic Earthquake Disaster Estimation." Procedia Computer Science 51 (2015): 1483–93. http://dx.doi.org/10.1016/j.procs.2015.05.338.

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Ramos-López, Darío, Andrés R. Masegosa, Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, and Anders L. Madsen. "Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks." International Journal of Approximate Reasoning 100 (September 2018): 115–34. http://dx.doi.org/10.1016/j.ijar.2018.06.004.

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