Pub Date : 2015-08-06DOI: 10.1109/ICME.2015.7177397
R. C. M. Santos, M. Moreno, L. Soares
This paper presents an architecture for monitoring the presentation of multimedia declarative applications, providing feedback about variables states, object properties, media presentation times, among others. Monitoring tools that follow the proposed architecture are able to detect if visual problems are being caused by programming errors or by player malfunctioning. The architecture presents a communication protocol designed to be independent of the declarative language used in the development of multimedia applications. The main goal is to provide an open and generic architecture that can assist multimedia application authors and presentation engine developers. As an example of the architecture use, the paper also presents a monitoring tool integrated into a graphical user interface developed for the ITU-T reference implementation of the Ginga-NCL middleware.
{"title":"An architecture to assist multimedia application authors and presentation engine developers","authors":"R. C. M. Santos, M. Moreno, L. Soares","doi":"10.1109/ICME.2015.7177397","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177397","url":null,"abstract":"This paper presents an architecture for monitoring the presentation of multimedia declarative applications, providing feedback about variables states, object properties, media presentation times, among others. Monitoring tools that follow the proposed architecture are able to detect if visual problems are being caused by programming errors or by player malfunctioning. The architecture presents a communication protocol designed to be independent of the declarative language used in the development of multimedia applications. The main goal is to provide an open and generic architecture that can assist multimedia application authors and presentation engine developers. As an example of the architecture use, the paper also presents a monitoring tool integrated into a graphical user interface developed for the ITU-T reference implementation of the Ginga-NCL middleware.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134209493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-08-06DOI: 10.1109/ICME.2015.7177381
Sharath Chandra Guntuku, S. Roy, Weisi Lin
Computationally modeling users `liking' for image(s) requires understanding how to effectively represent the image so that different factors influencing user `likes' are considered. In this work, an evaluation of the state-of-the-art visual features in multimedia understanding at the task of predicting user `likes' is presented, based on a collection of images crawled from Flickr. Secondly, a probabilistic approach for modeling `likes' based only on tags is proposed. The approach of using both visual and text-based features is shown to improve the state-of-the-art performance by 12%. Analysis of the results indicate that more human-interpretable and semantic representations are important for the task of predicting very subtle response of `likes'.
{"title":"Evaluating visual and textual features for predicting user ‘likes’","authors":"Sharath Chandra Guntuku, S. Roy, Weisi Lin","doi":"10.1109/ICME.2015.7177381","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177381","url":null,"abstract":"Computationally modeling users `liking' for image(s) requires understanding how to effectively represent the image so that different factors influencing user `likes' are considered. In this work, an evaluation of the state-of-the-art visual features in multimedia understanding at the task of predicting user `likes' is presented, based on a collection of images crawled from Flickr. Secondly, a probabilistic approach for modeling `likes' based only on tags is proposed. The approach of using both visual and text-based features is shown to improve the state-of-the-art performance by 12%. Analysis of the results indicate that more human-interpretable and semantic representations are important for the task of predicting very subtle response of `likes'.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131457850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-08-06DOI: 10.1109/ICME.2015.7177486
Duc-Tien Dang-Nguyen, Luca Piras, G. Giacinto, G. Boato, F. D. Natale
In this paper, we present a novel method that can produce a visual description of a landmark by choosing the most diverse pictures that best describe all the details of the queried location from community-contributed datasets. The main idea of this method is to filter out non-relevant images at a first stage and then cluster the images according to textual descriptors first, and then to visual descriptors. The extraction of images from different clusters according to a measure of user's credibility, allows obtaining a reliable set of diverse and relevant images. Experimental results performed on the MediaEval 2014 “Retrieving Diverse Social Images” dataset show that the proposed approach can achieve very good performance outperforming state-of-art techniques.
{"title":"A hybrid approach for retrieving diverse social images of landmarks","authors":"Duc-Tien Dang-Nguyen, Luca Piras, G. Giacinto, G. Boato, F. D. Natale","doi":"10.1109/ICME.2015.7177486","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177486","url":null,"abstract":"In this paper, we present a novel method that can produce a visual description of a landmark by choosing the most diverse pictures that best describe all the details of the queried location from community-contributed datasets. The main idea of this method is to filter out non-relevant images at a first stage and then cluster the images according to textual descriptors first, and then to visual descriptors. The extraction of images from different clusters according to a measure of user's credibility, allows obtaining a reliable set of diverse and relevant images. Experimental results performed on the MediaEval 2014 “Retrieving Diverse Social Images” dataset show that the proposed approach can achieve very good performance outperforming state-of-art techniques.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"741 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116089084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-08-06DOI: 10.1109/ICME.2015.7177476
L. Baraldi, C. Grana, R. Cucchiara
Scene detection is a fundamental tool for allowing effective video browsing and re-using. In this paper we present a model that automatically divides videos into coherent scenes, which is based on a novel combination of local image descriptors and temporal clustering techniques. Experiments are performed to demonstrate the effectiveness of our approach, by comparing our algorithm against two recent proposals for automatic scene segmentation. We also propose improved performance measures that aim to reduce the gap between numerical evaluation and expected results.
{"title":"Scene segmentation using temporal clustering for accessing and re-using broadcast video","authors":"L. Baraldi, C. Grana, R. Cucchiara","doi":"10.1109/ICME.2015.7177476","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177476","url":null,"abstract":"Scene detection is a fundamental tool for allowing effective video browsing and re-using. In this paper we present a model that automatically divides videos into coherent scenes, which is based on a novel combination of local image descriptors and temporal clustering techniques. Experiments are performed to demonstrate the effectiveness of our approach, by comparing our algorithm against two recent proposals for automatic scene segmentation. We also propose improved performance measures that aim to reduce the gap between numerical evaluation and expected results.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132409985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-08-06DOI: 10.1109/ICME.2015.7177448
Hui Liang, Junsong Yuan, D. Thalmann
Articulated hand pose recovery in egocentric vision is useful for in-air interaction with the wearable devices, such as the Google glasses. Despite the progress obtained with the depth camera, this task is still challenging with ordinary RGB cameras. In this paper we demonstrate the possibility to recover both the articulated hand pose and its distance from the camera with a single RGB camera in egocentric view. We address this problem by modeling the distance as a hidden variable and use the Conditional Regression Forest to infer the pose and distance jointly. Especially, we find that the pose estimation accuracy can be further enhanced by incorporating the hand part semantics. The experimental results show that the proposed method achieves good performance on both a synthesized dataset and several real-world color image sequences that are captured in different environments. In addition, our system runs in real-time at more than 10fps.
{"title":"Egocentric hand pose estimation and distance recovery in a single RGB image","authors":"Hui Liang, Junsong Yuan, D. Thalmann","doi":"10.1109/ICME.2015.7177448","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177448","url":null,"abstract":"Articulated hand pose recovery in egocentric vision is useful for in-air interaction with the wearable devices, such as the Google glasses. Despite the progress obtained with the depth camera, this task is still challenging with ordinary RGB cameras. In this paper we demonstrate the possibility to recover both the articulated hand pose and its distance from the camera with a single RGB camera in egocentric view. We address this problem by modeling the distance as a hidden variable and use the Conditional Regression Forest to infer the pose and distance jointly. Especially, we find that the pose estimation accuracy can be further enhanced by incorporating the hand part semantics. The experimental results show that the proposed method achieves good performance on both a synthesized dataset and several real-world color image sequences that are captured in different environments. In addition, our system runs in real-time at more than 10fps.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115073564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-07-03DOI: 10.1109/ICME.2015.7177517
Cheng Yang, Gene Cheung, V. Stanković
Depth sensors like Microsoft Kinect can acquire partial geometric information in a 3D scene via captured depth images, with potential application to non-contact health monitoring. However, captured depth videos typically suffer from low bit-depth representation and acquisition noise corruption, and hence using them to deduce health metrics that require tracking subtle 3D structural details is difficult. In this paper, we propose to capture depth video using Kinect 2.0 to estimate the heart rate of a human subject; as blood is pumped to circulate through the head, tiny oscillatory head motion can be detected for periodicity analysis. Specifically, we first perform a joint bit-depth enhancement / denoising procedure to improve the quality of the captured depth images, using a graph-signal smoothness prior for regularization. We then track an automatically detected nose region throughout the depth video to deduce 3D motion vectors. The deduced 3D vectors are then analyzed via principal component analysis to estimate heart rate. Experimental results show improved tracking accuracy using our proposed joint bit-depth enhancement / denoising procedure, and estimated heart rates are close to ground truth.
{"title":"Estimating heart rate via depth video motion tracking","authors":"Cheng Yang, Gene Cheung, V. Stanković","doi":"10.1109/ICME.2015.7177517","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177517","url":null,"abstract":"Depth sensors like Microsoft Kinect can acquire partial geometric information in a 3D scene via captured depth images, with potential application to non-contact health monitoring. However, captured depth videos typically suffer from low bit-depth representation and acquisition noise corruption, and hence using them to deduce health metrics that require tracking subtle 3D structural details is difficult. In this paper, we propose to capture depth video using Kinect 2.0 to estimate the heart rate of a human subject; as blood is pumped to circulate through the head, tiny oscillatory head motion can be detected for periodicity analysis. Specifically, we first perform a joint bit-depth enhancement / denoising procedure to improve the quality of the captured depth images, using a graph-signal smoothness prior for regularization. We then track an automatically detected nose region throughout the depth video to deduce 3D motion vectors. The deduced 3D vectors are then analyzed via principal component analysis to estimate heart rate. Experimental results show improved tracking accuracy using our proposed joint bit-depth enhancement / denoising procedure, and estimated heart rates are close to ground truth.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131427500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-29DOI: 10.1109/ICME.2015.7177406
Erwan Nogues, Romain Berrada, M. Pelcat, D. Ménard, E. Raffin
Software video decoders for mobile devices are now a reality thanks to recent advances in Systems-on-Chip (SoC). The challenge has now moved to designing energy efficient systems. In this paper, we propose a light Dynamic Voltage Frequency Scaling (DVFS)-enabled software adapted to the much varying processing load of High Efficiency Video Coding (HEVC) real-time decoding. We analyze a practical evaluation of a HEVC decoder using our proposal on a Samsung Exynos low-power SoC widely used in portable devices. Experimental results show more than 50% of power savings on a real-time decoding when compared to the same software managed by the OnDemand Linux power management. For mobile applications, the proposed method can achieve 720p video HEVC decoding at 60 frames per second consuming approximately 1.1W with pure software decoding on a general purpose processor.
{"title":"A DVFS based HEVC decoder for energy-efficient software implementation on embedded processors","authors":"Erwan Nogues, Romain Berrada, M. Pelcat, D. Ménard, E. Raffin","doi":"10.1109/ICME.2015.7177406","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177406","url":null,"abstract":"Software video decoders for mobile devices are now a reality thanks to recent advances in Systems-on-Chip (SoC). The challenge has now moved to designing energy efficient systems. In this paper, we propose a light Dynamic Voltage Frequency Scaling (DVFS)-enabled software adapted to the much varying processing load of High Efficiency Video Coding (HEVC) real-time decoding. We analyze a practical evaluation of a HEVC decoder using our proposal on a Samsung Exynos low-power SoC widely used in portable devices. Experimental results show more than 50% of power savings on a real-time decoding when compared to the same software managed by the OnDemand Linux power management. For mobile applications, the proposed method can achieve 720p video HEVC decoding at 60 frames per second consuming approximately 1.1W with pure software decoding on a general purpose processor.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121218027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-29DOI: 10.1109/ICME.2015.7177514
C. Mollaret, Alhayat Ali Mekonnen, I. Ferrané, J. Pinquier, F. Lerasle
Understanding people's intention, be it action or thought, plays a fundamental role in establishing coherent communication amongst people, especially in non-proactive robotics, where the robot has to understand explicitly when to start an interaction in a natural way. In this work, a novel approach is presented to detect people's intention-for-interaction. The proposed detector fuses multimodal cues, including estimated head pose, shoulder orientation and vocal activity detection, using a probabilistic discrete state Hidden Markov Model. The multimodal detector achieves up to 80% correct detection rates improving purely audio and RGB-D based variants.
{"title":"Perceiving user's intention-for-interaction: A probabilistic multimodal data fusion scheme","authors":"C. Mollaret, Alhayat Ali Mekonnen, I. Ferrané, J. Pinquier, F. Lerasle","doi":"10.1109/ICME.2015.7177514","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177514","url":null,"abstract":"Understanding people's intention, be it action or thought, plays a fundamental role in establishing coherent communication amongst people, especially in non-proactive robotics, where the robot has to understand explicitly when to start an interaction in a natural way. In this work, a novel approach is presented to detect people's intention-for-interaction. The proposed detector fuses multimodal cues, including estimated head pose, shoulder orientation and vocal activity detection, using a probabilistic discrete state Hidden Markov Model. The multimodal detector achieves up to 80% correct detection rates improving purely audio and RGB-D based variants.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114837247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-01DOI: 10.1109/ICME.2015.7177461
Yemin Shi, Wei Zeng, Tiejun Huang, Yaowei Wang
Human action recognition is widely recognized as a challenging task due to the difficulty of effectively characterizing human action in a complex scene. Recent studies have shown that the dense-trajectory-based methods can achieve state-of-the-art recognition results on some challenging datasets. However, in these methods, each dense trajectory is often represented as a vector of coordinates, consequently losing the structural relationship between different trajectories. To address the problem, this paper proposes a novel Deep Trajectory Descriptor (DTD) for action recognition. First, we extract dense trajectories from multiple consecutive frames and then project them onto a canvas. This will result in a “trajectory texture” image which can effectively characterize the relative motion in these frames. Based on these trajectory texture images, a deep neural network (DNN) is utilized to learn a more compact and powerful representation of dense trajectories. In the action recognition system, the DTD descriptor, together with other non-trajectory features such as HOG, HOF and MBH, can provide an effective way to characterize human action from various aspects. Experimental results show that our system can statistically outperform several state-of-the-art approaches, with an average accuracy of 95:6% on KTH and an accuracy of 92.14% on UCF50.
{"title":"Learning Deep Trajectory Descriptor for action recognition in videos using deep neural networks","authors":"Yemin Shi, Wei Zeng, Tiejun Huang, Yaowei Wang","doi":"10.1109/ICME.2015.7177461","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177461","url":null,"abstract":"Human action recognition is widely recognized as a challenging task due to the difficulty of effectively characterizing human action in a complex scene. Recent studies have shown that the dense-trajectory-based methods can achieve state-of-the-art recognition results on some challenging datasets. However, in these methods, each dense trajectory is often represented as a vector of coordinates, consequently losing the structural relationship between different trajectories. To address the problem, this paper proposes a novel Deep Trajectory Descriptor (DTD) for action recognition. First, we extract dense trajectories from multiple consecutive frames and then project them onto a canvas. This will result in a “trajectory texture” image which can effectively characterize the relative motion in these frames. Based on these trajectory texture images, a deep neural network (DNN) is utilized to learn a more compact and powerful representation of dense trajectories. In the action recognition system, the DTD descriptor, together with other non-trajectory features such as HOG, HOF and MBH, can provide an effective way to characterize human action from various aspects. Experimental results show that our system can statistically outperform several state-of-the-art approaches, with an average accuracy of 95:6% on KTH and an accuracy of 92.14% on UCF50.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125101381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-01DOI: 10.1109/ICME.2015.7177435
Christopher Müller, Stefan Lederer, Reinhard Grandl, C. Timmerer
Streaming multimedia over the Internet is omnipresent but still in its infancy, specifically when it comes to the adaptation based on bandwidth/throughput measurements, clients competing for limited/shared bandwidth, and the presence of a caching infrastructure. In this paper we present a buffer-based adaptation logic in combination with a toolset of client metrics to compensate for erroneous adaptation decisions. These erroneous adaptation decisions are due to insufficient network information available at the client and issues introduced when multiple clients compete for limited/shared bandwidth and/or when caches are deployed. Our metrics enable the detection of oscillations on the client - in contrast to server-based approaches - and provide an effective compensation mechanism. We evaluate the proposed adaptation logic, which incorporates the oscillation detection and compensation method, and compare it against a throughput-based adaptation logic for scenarios comprising competing clients with and without caching enabled. In anticipation of the results, we show how the presented metrics detect oscillation periods and how such undesirable situations can be compensated while increasing the effective media throughput of the clients.
{"title":"Oscillation compensating Dynamic Adaptive Streaming over HTTP","authors":"Christopher Müller, Stefan Lederer, Reinhard Grandl, C. Timmerer","doi":"10.1109/ICME.2015.7177435","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177435","url":null,"abstract":"Streaming multimedia over the Internet is omnipresent but still in its infancy, specifically when it comes to the adaptation based on bandwidth/throughput measurements, clients competing for limited/shared bandwidth, and the presence of a caching infrastructure. In this paper we present a buffer-based adaptation logic in combination with a toolset of client metrics to compensate for erroneous adaptation decisions. These erroneous adaptation decisions are due to insufficient network information available at the client and issues introduced when multiple clients compete for limited/shared bandwidth and/or when caches are deployed. Our metrics enable the detection of oscillations on the client - in contrast to server-based approaches - and provide an effective compensation mechanism. We evaluate the proposed adaptation logic, which incorporates the oscillation detection and compensation method, and compare it against a throughput-based adaptation logic for scenarios comprising competing clients with and without caching enabled. In anticipation of the results, we show how the presented metrics detect oscillation periods and how such undesirable situations can be compensated while increasing the effective media throughput of the clients.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130345679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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