• ETRI Journal
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• v.29 no.6
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• pp.823-825
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• 2007

In this letter, we propose a novel algorithm to predict MPEG-coded real-time variable bit rate (VBR) video traffic. From the frame size measurement, the algorithm extracts the statistical property of video traffic and utilizes it for the prediction of the next frame for I-, P-, and B- frames. The simulation results conducted with real-world MPEG-4 VBR video traces show that the proposed algorithm is capable of providing more accurate prediction than those in the research literature.

• Journal of KIISE:Information Networking
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• v.34 no.3
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• pp.156-167
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• 2007

In this paper, we develop the model based prediction algorithm for Variable-Bit-Rate(VBR) video traffic with regular Group of Picture(GOP) pattern. We use multiplicative ARIMA process called GOP ARIMA (ARIMA for Group Of Pictures) as a base stochastic model. Kalman Filter based prediction algorithm consists of two process: GOP ARIMA modeling and prediction. In performance study, we produce three video traces (news, drama, sports) and we compare the accuracy of three different prediction schemes: Kalman Filter based prediction, linear prediction, and double exponential smoothing. The proposed prediction algorithm yields superior prediction accuracy than the other two. We also show that confidence interval analysis can effectively detect scene changes of the sample video sequence. The Kalman filter based prediction algorithm proposed in this work makes significant contributions to various aspects of network traffic engineering and resource allocation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3635-3654
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• 2015

With the rapid development of smart devices and mobile Internet, the video application plays an increasingly important role on mobile devices. Understanding user behavior patterns is critical for optimized operation of mobile live streaming systems. On the other hand, volume based billing models on cloud services make it easier for video service providers to scale their services as well as to reduce the waste from oversized service capacities. In this paper, the watching behaviors of a commercial mobile live streaming system are studied in a content-centric manner. Our analysis captures the intrinsic correlation existing between popularity and watching intensity of programs due to the synchronized watching behaviors with program schedule. The watching pattern is further used to estimate traffic volume generated by the program, which is useful on data volume capacity reservation and billing strategy selection in cloud services. The traffic range of programs is estimated based on a naive popularity prediction. In cross validation, the traffic ranges of around 94% of programs are successfully estimated. In high popularity programs (>20000 viewers), the overestimated traffic is less than 15% of real happened traffic when using upper bound to estimate program traffic.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8A
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• pp.879-888
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• 2004

In this paper, we propose a real time QoS(Quality of Service) guaranteed transmission mechanism for MPEG video traffic at the congested node in IP based networks. Recent spread of the Internet has increased the demands of a real time multimedia service of the quality, Because the type of Internet services can, however, offer the best effort delivery strategies, it is difficult to treat all the types of traffic with differential COS (Class of Service). Most of all, the hierarchical coding method of MPEG data utilizes the reference frame for the motion prediction. The loss of the reference frames makes QoS of the video traffic degraded because the reference frame bit error causes the consecutive packet loss. Therefore we have studied the real time QoS guaranteed mechanism for video traffic by analyzing the previous methods. Computer simulation results show that the proposed scheme has better performance than the previous one.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.229-234
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• 2021

The explosive growth of video-based services is considered as the dominant contributor to Internet traffic. Hence it is very important for video service providers to meet the quality expectations of end-users. In the past, the Quality of Service (QoS) was the key performance of networks but it considers only the network performances (e.g., bandwidth, delay, packet loss rate) which fail to give an indication of the satisfaction of users. Therefore, Quality of Experience (QoE) may allow content servers to be smarter and more efficient. This work is motivated by the inherent relationship between the QoE and the QoS. We present a no-reference (NR) prediction model based on Deep Neural Network (DNN) to predict video QoE. The DNN-based model shows a high correlation between the objective QoE measurement and QoE prediction. The performance of the proposed model was also evaluated and compared with other types of neural network architectures, and three known machine learning methodologies, the performance comparison shows that the proposed model appears as a promising way to solve the problems.

• Journal of Korea Multimedia Society
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• v.8 no.9
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• pp.1216-1226
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• 2005

In this paper, a preventive transmission rate decision algorithm, called PTRD, is proposed for the transmission of live video over networks with dynamic bandwidth allocation capability. Frame analyzer predicts the bit-rates of future frames before encoding by analyzing the source information such as spatial variances and the degree of scene changes. By using the predicted bit-rates, transmission rate bounds are derived from the constraints of encoder and decoder buffers. To resolve the problem of renegotiation cost increment due to frequent renegotiations, the PTRD algorithm is presented to decide transmission rates considering the elapsed time after the recent renegotiation and the perceived video quality. From the simulation results, compared to the normalized LMS based method, PTRD is shown to achieve high channel utilization with low renegotiation cost and no delay violation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1A
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• pp.142-155
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• 2000

In this paper, we propose an efficient policing mechanism for Markov model-based traffic descriptors of VBR MPEG video traffic. A VBR video sequence is described by a set of traffic descriptors using a scene-basedMarkov model to the network for the effective resource allocation and accurate QoS prediction. The networkmonitors the input traffic from the source using a proposed new policing method. for policing the steady statetransition probability of scene states, we define two representative monitoring parameters (mean holding andrecurrence time) for each state. For frame level cell rate policing of each scene state, accumulated average cellrates for the frame types are compared with the model parameters. We propose an exponential bounding functionto accommodate dynanic behaviors during the transient period. Our simulation results show that the proposedpolicing mechanism for Markovian traffic descriptors monitors the sophisticated traffic such as MPEG videoeffectively and well protects network resources from the nalicious or misbehaved traffic.

• Journal of Industrial Convergence
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• v.18 no.3
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• pp.53-61
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• 2020

Travel speed is an important parameter for measuring road traffic and incident detection system. In this paper I suggests a model developed for estimating reliable and accurate average roadway link travel speeds using image processing sensor. This method extracts the vehicles from the video image from CCTV, tracks the moving vehicles using deep neural network, and extracts traffic information such as link travel speeds and volume. The algorithm estimates link travel speeds using a robust data-fusion procedure to provide accurate link travel speeds and traffic information to the public. In the field tests, the new model performed better than existing methods.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.297-300
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• 2001

본 논문에서는 동영상의 계층적 부호화의 효율을 높이기 위한 방안에 대해 연구하였다. 단일 계층부호화에 비해 다 계층부호화는 계산량이 많아진다. 따라서 계층적 부호화의 장점을 살리고 단점을 보완하는 방안을 제시하였다. 우선 인코더에서 고급계층의 복잡도를 줄이기 위하여 고급계층의 참조 형태를 P-VOP (Prediction-Video Object Plane)만으로 정한다. 고급계층의 참조 영역으로 사용되는 업샘플링된 VOP의 횟수를 줄여서 업샘플링에 따른 계산량을 줄인다. 그리고 고급계층의 비트율을 조절하여 Traffic shaping 효과도 얻을 수 있다. 이러한 방법들을 통해 단일 계층 부호화에 비해 다 계층부호화의 장점을 살리고 단점을 보완하는 코덱을 제안한다.

• Journal of Intelligence and Information Systems
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• v.3 no.1
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• pp.31-45
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• 1997

To date, research on data mining has focused primarily on individual techniques to su, pp.rt knowledge discovery. However, the integration of elementary learning techniques offers a promising strategy for challenging a, pp.ications such as forecasting nonlinear processes. This paper explores the utility of an integrated a, pp.oach which utilizes a second-order learning process. The a, pp.oach is compared against individual techniques relating to a neural network, case based reasoning, and induction. In the interest of concreteness, the concepts are presented through a case study involving the prediction of network traffic for video-on-demand.