ACM Transactions on

Multimedia Computing, Communications, and Applications (TOMM)

Latest Articles

When Game Becomes Life

Online gaming franchises such as World of Tanks, Defense of the Ancients, and StarCraft have attracted hundreds of millions of users who, apart from playing the game, also socialize with each other through gaming and viewing gamecasts. As a form of User Generated Content (UGC), gamecasts play an important role in user entertainment and gamer... (more)

Depth-Based View-Invariant Blind 3D Image Watermarking

With the huge advance in Internet technology as well as the availability of low-cost 3D display devices, 3D image transmission has become popular in... (more)

Audio Masking Effect on Inter-Component Skews in Olfaction-Enhanced Multimedia Presentations

Media-rich content plays a vital role in consumer applications today, as these applications try to find new and interesting ways to engage their... (more)

Field Effect Deep Networks for Image Recognition with Incomplete Data

Image recognition with incomplete data is a well-known hard problem in computer vision and machine learning. This article proposes a novel deep... (more)

A Unified Video Recommendation by Cross-Network User Modeling

Online video sharing sites are increasingly encouraging their users to connect to the social network venues such as Facebook and Twitter, with goals... (more)

Semantic Feature Mining for Video Event Understanding

Content-based video understanding is extremely difficult due to the semantic gap between low-level vision signals and the various semantic concepts... (more)


[June 2016]

Forthcoming Special Issues in 2017

We received 11 competitive proposals this year, and we had limited slots available, so it was a very tough decision. At the end, the following four SI proposals have been selected and scheduled as follows:

- "Deep Learning for Mobile Multimedia". Cfp  Submission deadline Oct. 15, 2016

- "Delay-Sensitive Video Computing in the Cloud". Cfp Submission deadline Nov. 30, 2016

- "Representation, Analysis and Recognition of 3D Human". Cfp   Submission deadline Jan. 15, 2017 

- "QoE Management for Multimedia Services". Cfp Submission deadline May 15, 2017

Two Special Section have been also accepted and scheduled for publication in 2017:

- "Multimedia Computing and Applications of Socio-Affective Behaviors in the Wild". Cfp Submission deadline Oct. 31, 2016

- "Multimedia Understanding via Multimodal Analytics". Cfp Submission deadline Oct. 31, 2016

[June 2016]

Forthcoming Special Issues in 2016

"Trust Management for Multimedia Big Data" - Publication date August 2016

"Multimedia Big Data: Networking" - Publication date November 2016

[February 2016]

Advisory Board

We have created the ACM TOMM Advisory Board to support the Editor in Chief in the definition and implementation of strategies with no editorial duties. The following colleagues have been appointed as members of the ACM TOMM Advisory Board: Prof. Wen Gao,  Peking University, Prof. Arnold Smeulders, University of Amsterdam, Prof. Nicu Sebe, University of Trento. 

[January 2016]

New Assistant Information Director

Starting on January 1st 2016, Marco Bertini will be in charge of Assistant Information Director of ACM TOMM. 

[January 2016]

New Information Director         Starting on January 1st 2016, Stefano Berretti will be in charge of Information Director of ACM TOMM.  

[January 2016]

New Editor-in-Chief

After the end of the second term of Ralf Steinmetz, Alberto Del Bimbo from the University of Florence will be the next TOMM Editor-in-Chief starting on January 1st 2016. 

ACM TOMM Nicolas D. Georgans Best Paper Award 2015

The award goes to the article "A Quality of Experience Model for Haptic Virtual Environments” (TOMM vol.10, Issue 3) by Abdelwahab Hamam, Abdulmotaleb El Saddik and Jihad Alja'am. Congratulations!

ACM TOMM Associate Editor of the Year 2015 Award

The award goes Pradeep Atrey from State University of New York, USA for his excellent work for the journal. Congratulations!

CfP: Special Issue "Multimedia Big Data: Networking"

Please consider submitting to the second special issue in next years special issue series. Call for Papers


CfP: Special Issue "Trust Management for Multimedia Big Data"

Next year, TOMM will feature a special issue series on "Multimedia Big Data". First topic will be "Trust Management". Extended Deadline: October 15th! Call for Papers


Call for Nominations TOMM Editor-in-Chief

After two terms of the current EiC Ralf Steinmetz, the search committee started the search for a new Editor-in-Chief. Call for Nominations


New ACM submission templates

The new ACM submission templates are online. Please use the most recent link on the authors' guide to find the files.


About TOMM


A peer-reviewed, quarterly archival journal in print and digital form, TOMM consists primarily of research papers of lasting importance and value in the field of multimedia computing, communications and applications. 

News archive
Forthcoming Articles

Trust Management for Multimedia Big Data

A Deployment Optimization Scheme over Multimedia Big Data for LargeScale Media Streaming Application

With the prosperity of media streaming application over the Internet in the past decades, multimedia data sharply increase (categorized as multimedia big data) which exerts more pressure on the infrastructure such as networking of application provider. In order to move this hurdle, it is a recent trend that an increasing number of traditional media streaming applications are migrated from private server cluster onto cloud. With the elastic resource provisioning and centralized management of cloud, the operational costs of media streaming application provider can decrease dramatically. However, to the best of our knowledge, existing migration solutions do not fully take viewer information such as hardware condition into consideration. In this paper, we consider the deployment optimization problem named ODP by leveraging local memories at each viewer. Considering the NP-hardness of calculating the optimal solution, we turn to propose computationally tractable algorithms. Specifically, we unfold the original problem to be two interactive subproblems, i.e., coarse-grained migration subproblem and fine-grained scheduling subproblem. Then, the corresponding offline approximation algorithms with performance guarantee and computational efficiency are given. The results of extensive evaluation show that compared with the baseline algorithm without leveraging local memories at viewers, our proposed algorithms and their online versions can decrease total bandwidth reservation and enhance the utilization of bandwidth reservation dramatically.

Mobile Video Streaming over Dynamic Single Frequency Networks

The demand for multimedia streaming over mobile networks has been steadily increasing in the past several years. For instance, it has become common for mobile users to stream full TV episodes, sports events, and movies while on the go. Unfortunately, this growth in demand has strained the wireless networks despite the significant increase of their capacities with recent generations. Hence, efficient utilization of the expensive and limited wireless spectrum remains an important problem, especially in the context of multimedia streaming services that consume a large portion of the bandwidth capacity. In this paper, we introduce the idea of dynamically configuring cells in wireless networks to form single frequency networks based on the multimedia traffic demands from users in each cell. We formulate the resource allocation problem in such complex networks with the goal of maximizing the number of served multimedia streams, and we prove that this problem is NP-Complete. Then we present an optimal solution to maximize the number of served multimedia streams within a cellular network. This optimal solution, however, may suffer from an exponential time complexity in the worst case, which is not practical for real-time streaming over large-scale networks. Therefore, we propose a heuristic algorithm with polynomial running time to provide faster and more practical solution for real-time deployments. Through detailed packet-level simulations, we assess the performance of the proposed algorithms with respect to the average service ratio, energy saving, video quality, frame loss rate, initial buffering time, rate of re-buffering events, and bandwidth overhead. We show that the proposed algorithms achieve substantial improvements in all of these performance metrics compared to the state-of-the-art approaches. For example, for the service ratio metric, our algorithms can serve up to 11 times more users compared to the unicast streaming approach, and they achieve up to 54% improvement over the closest multicast approach in the literature.

A Tensor-based Framework for Software-defined Cloud Data Center

Multimedia has been exponentially increasing as the biggest big data, which consist of video clips, images and audio files. Processing and analyzing them on cloud data center have become a preferred solution which can utilize the large pool of cloud resources to address the problems caused by the tremendous amount of unstructured multimedia data. However, there exists many challenges in the approaches to process multimedia big data on cloud data center such as multimedia data representation, efficient networking model as well as estimation of traffic patterns. The primary purpose of this paper is to develop a novel tensor-based software-defined networking model on cloud data center for multimedia big data computation and communication. Firstly, we provide an overview of the proposed framework and briefly illustrate the functions of the representative modules. Then three models, namely, forwarding tensor, controlling tensor and transition tensor are proposed for management of networking devices and prediction of network traffics. Finally, two algorithms about single-mode and multi-mode tensor eigen-decomposition are developed, and the incremental method is employed for efficiently updating the generated eigen-vector and eigen-tensor. Experimental results reveals that the proposed framework is feasible and efficient to handle the multimedia big data on cloud data center.

Device-to-Device Communication for Mobile Multimedia in Emerging 5G Networks

Device-to-device (D2D) communication which utilizes mobile devices located within close proximity for direct connection and data exchange, holds great promise for improving energy and spectrum efficiency of mobile multimedia in 5G networks. It is noticed that most available D2D based works considered only the single cell scenario with a single BS, although tractable and able to illustrate the relationship between D2D links and cellular links, failed to take into account the distribution of surrounding base stations and UEs, as well as the accumulated interference from ongoing transmissions in other cells. Furthermore, the single tier network with one BS considered in available works, is far from the real 5G scenario in which multi-tier BSs are heterogeneously distributed among the whole network area. In light of such observations, we present in this paper a model for average rate analysis in a D2D communication overlaying two-tier downlink cellular network, where nineteen macro base stations (MBSs) with pico base stations (PBSs) placed at the end point of macro cell (hexagons) borders are employed according to the 3GPP specifications, and mobile users are spatially distributed according to the homogeneous Poisson Point Process model. Each mobile UE is able to establish D2D link with adjacent UEs or connect to a nearby macro or pico base station. Stochastic geometric analysis is adopted to characterize the intra-tier interference distribution within MBS-tier, PBS-tier, as well as the D2D-tier, based on which the per user average rate is derived with a careful consideration of the important issues such as user density, content hit rate, spectrum allocation, and cell coverage range. Our results show that even for the overlaying case, D2D communication can significantly improve the per user average downlink rate. Another finding is that the frequency allocation for D2D communications should be carefully tuned according to network settings, which may result in totally different varying behaviors for the per user average rate.

Approximate Asymmetric Search for Binary Embedding Codes

In this paper, we propose a method of approximate asymmetric nearest neighbor search for binary embedding codes. Asymmetric distances take advantage of less information loss at the query side. However, calculating asymmetric distances with the linear scan approach is prohibitive in a large-scale dataset. We present a novel method called multi-index voting, which integrates the multi-index hashing technique with a voting mechanism, to select appropriate candidates and calculate their asymmetric distances. We show that the candidate selection scheme can be formulated as the tail of the binomial distribution function. In addition, a binary feature selection method based on minimal quantization error is proposed to improve the search accuracy. Substantial experimental evaluations are given to demonstrate that the proposed method can yield an approximate accuracy to the exhaustive search method while accelerating the run time significantly. For example, one result shows that in a dataset of one billion 256-bit binary codes, examining only 0.5% of the dataset can reach 95~99% close accuracy to the exhaustive search method and accelerate over 73~128 times. It also demonstrates an excellent tradeoff between the search accuracy and time efficiency compared to the state-of-the-art nearest neighbor search methods. Moreover, the proposed feature selection method shows its effectiveness and improves the accuracy up to 8.35% compared with the baseline method.

Towards Delay-Efficient Game-Aware Data Centers For Cloud Gaming

Gaming on demand is an emerging service that has recently started to garner prominence in the gaming industry. Cloud based video games provide affordable, flexible and high performance solution for end-users with constrained computing resources and enables them to play high-end graphic games on low-end thin clients. Despite its advantages, cloud gamings Quality of Experience (QoE) suffers from high and varying end-to-end delay. Since the significant part of computational processing, including game rendering and video compression, is performed in data centers, controlling the transfer of information within the cloud has an important impact on the quality of cloud gaming services. In this paper, a novel method for minimizing the end-to-end latency within a cloud gaming data center is proposed. We formulate an optimization problem for reducing delay, and we also propose a Lagrangean Relaxation (LR) time-efficient heuristic algorithm as a practical solution. Simulation results indicate that the heuristic method can provide close-to-optimal solutions. Also, the proposed model reduces end-to-end delay and delay variation by almost 11% and 13.5%, respectively, and outperforms the existing server-centric and network-centric models. As a byproduct, our proposed method also achieves better fairness among multiple competing players by almost 45% on average in comparison with existing methods.

Progressive Visual Cryptography with Unexpanded Meaningful Shares.

The traditional k out of n Visual Cryptography (VC) scheme is the conception of All or Nothing for n participants to share a secret image. The original secret image can be visually revealed only when a subset of k or more shares are superimposed together, but if the number of stacked shares are less than k, nothing will be revealed. On the other hand Progressive Visual Cryptography (PVC) scheme differs from the traditional VC with respect to decoding point of view. In the PVC, clarity and contrast of the decoded secret image will be increased progressively with the number of stacked shares. Many existing state of art researches on PVC have problems of pixel expansion and random pattern of the shares. In this paper, a novel scheme of progressive visual cryptography with four or more number of unexpanded as well as meaningful shares has been proposed. For this, a novel and efficient Candidate Block Replacement (CBR) preprocessing approach and a basis matrix creation algorithm have also been introduced. Proposed method also eliminates many unnecessary encryption constraints like predefined codebook for encoding and decoding the secret image, restriction on the number of participants etc. From the experiments, it is observed that the reconstruction probability of black pixels in the decoded image corresponding to the black pixel in the secret image is always 1 whereas of white pixel is 0.5 irrespective of the meaningful contents visible in the shares. Thus insuring the value of contrast to be 50% always. Therefore reconstructed image can be easily identified by human visual system without any computation.

Video Management and Resource Allocation for a Large-scale VoD Cloud

We study an optimization problem of providing Netflix-like video-on-demand (VoD) service for blockbusters on a cloud platform, where the content provider rents proxy servers placed close to user pools. Videos may have heterogeneous popularity among different geo-locations. The proxy servers collaboratively store and stream videos, and a repository provides video backup for the network. The rental resources for the cloud deployment consist of link capacities between servers, server storage, and server processing capacity to handle remote requests. Such cloud platform can be generalized to serve large multimedia files with bandwidth constraints. We study how to minimize the deployment cost by jointly optimizing resource allocation (in terms of link, storage, and processing capacities) and video management (in terms of video placement and retrieval), subject to a certain user quality-of-service (QoS) requirement. We first formulate the joint optimization problem and show that it is NP-hard. To address it, we propose RAVO, a novel and efficient algorithm with proven optimality gap based on linear programming. For large video pool, we propose a video clustering algorithm to substantially reduce the computational complexity without compromising on performance. Through extensive simulation, RAVO is shown to achieve close-to-optimal performance even for very large video pool, outperforming other schemes significantly (often by multiple times).

Performance Modelling and Analysis of Software Defined Networking under Bursty Multimedia Traffic

Software Defined Networking (SDN) has emerged as a promising next-generation Internet architecture for providing unprecedented network programmability to handle the explosive growth of Big Data driven by the popularisation of smart mobile devices and the pervasiveness of content-rich multimedia applications. In order to quantitatively investigate the performance limits of SDN networks, several research achievements from both simulation experiments and analytical modelling have been reported in the current literature. Among those studies, analytical modelling has demonstrated its superiority in terms of cost-effectiveness in the evaluation of large-scale, e.g., Internet-scale networks. However, for tractability and simplicity, existing analytical models are derived based on the unrealistic assumptions that all network traffic follows the Poisson process which is suitable to model nonbursty text data. Recent measurement studies have shown that, due to the features of heavy volume and high velocity, the multimedia big data generated by real-world multimedia applications reveals the bursty and correlated nature in the network transmission. With the aim of the capturing such characteristics of realistic traffic patterns and obtaining a comprehensive and deeper understanding of the performance behaviour of SDN networks, this paper presents a new analytical model to investigate the performance of SDN in the presence of the bursty and correlated arrivals modelled by Markov modulated Poisson process. The Quality-of-Service (QoS) performance metric in terms of average latency of the SDN networks are derived based on the developed analytical model. To consider multi-queue system of forwarding elements, a Priority-Queue (PQ) system is adopted to model SDN data plane. To ad- dress the challenging problem of obtaining the key performance metrics, e.g., queue length distribution of PQ system with a given service capacity, a versatile methodology extending the Empty Buffer Approxima- tion (EBA) method is proposed to facilitate the decomposition of such a PQ system to two Single Server Single Queue (SSSQ) systems. The validity of the proposed model is demonstrated through extensive simulation experiments. To illustrate its application, the developed model is then utilised to study the strategy of network configuration and resource allocation in SDN networks.

A Content-aware Video Adaptation Service to Support Mobile Video

The rising demand for mobile video is an increasing challenge for cellular networks. Even though they can be ubiquitously accessed by mobile devices the achieved throughputs are rather limited and highly varying. Nowadays, a solution which is deployed to cope with varying network conditions are adaptive video streaming systems. Adaptive video streaming approaches choose the appropriate video quality representations according to the available network resources and can dynamically adjust the quality during the streaming session. Even if the necessary throughput is available, mobile users are interested in limiting the generated data traffic as most cellular network contracts have data caps. Usually, if the cap is reached, the bandwidth is throttled to a speed which does not allow video streaming or requires additional payments. Existing systems react to varying network conditions but often neglect content-specific adaptation needs. Content inspection can help to save bandwidth when an increased video quality representation would not increase the perceived quality. In this work, we present a support service for a content-aware video adaptation on mobile devices. Based on the video content the adaptation process is improved for both the available network resources and the perception of the user. By leveraging the content properties of a video stream, the system is able to keep a stable video quality and at the same time reduce the generated data traffic. The system is evaluated with different adaptation schemes and shows that content specific adaptation can both increase the perceived quality as well as reduce the data traffic. Additionally, we integrate the VAS system into the current Dynamic Adaptive Streaming over HTTP (DASH) standard and show how such a system can be deployed.

QoE-Based Low-Delay Live Streaming Using Throughput Predictions

Recently, HTTP-based adaptive streaming has become the de facto standard for video streaming over the Internet. It allows clients to dynamically adapt media characteristics to varying network conditions in order to ensure a high quality of experience, that is, minimize playback interruptions, while maximizing video quality at a reasonable level of quality changes. In the case of live streaming, this task becomes particularly challenging due to the latency constraints. The challenge further increases if a client uses a wireless access network, where the throughput is subject to considerable fluctuations. Consequently, live streams often exhibit latencies of up to 20 to 30 seconds. In the present work, we introduce an adaptation algorithm for HTTP-based live streaming called LOLYPOP (Low-Latency Prediction-Based Adaptation) that is designed to operate with a transport latency of few seconds. To reach this goal, LOLYPOP leverages TCP throughput predictions on multiple time scales, from 1 to 10 seconds, along with an estimate of the relative prediction error distribution. In addition to satisfying the latency constraint, the algorithm heuristically maximizes the quality of experience by maximizing the average video quality as a function of the number of skipped segments and quality transitions. In order to select an efficient prediction method, we studied the performance of several time series prediction methods in IEEE 802.11 wireless access networks. We evaluated LOLYPOP under a large set of experimental conditions, limiting the transport latency to 3 seconds, against a state-of-the-art adaptation algorithm from the literature, called FESTIVE. We observed that the average video quality is by up to a factor of 3 higher than with FESTIVE. We also observed that LOLYPOP is able to reach a broader region in the quality of experience space, and thus it is better adjustable to the user profile or service provider requirements.

Introduction to: Special Issue on Multimedia Big Data: Networking

Congestion-aware MAC Layer Adaptation to Improve Video Telephony over Wi-Fi

In wireless networks such as those based on IEEE 802.11, packet losses due to fading and interference are often misinterpreted as indications of congestion by the congestion control protocol at the higher layers, causing unnecessary decrease in the data sending rate. For delay-constrained applications such as video telephony, packet losses may result in excessive artifacts or freeze in the decoded video. We propose a simple and yet effective mechanism to detect and reduce channel-caused packet losses by adjusting the retry limit parameter of the IEEE 802.11 protocol while taking into account the delay requirement of the traffic. Since the retry limit is left configurable in the IEEE 802.11 standard, and does not require cross-layer coordination, our scheme can be easily implemented and incrementally deployed. Experimental results of applying the proposed scheme to a WebRTC-based real-time video communication prototype show significant performance gain compared to the case where the retry limit is configured statically.


Publication Years 2005-2016
Publication Count 455
Citation Count 2482
Available for Download 454
Downloads (6 weeks) 2268
Downloads (12 Months) 24854
Downloads (cumulative) 215136
Average downloads per article 474
Average citations per article 5
First Name Last Name Award
El Saddik Abdulmotaleb ACM Distinguished Member (2010)
ACM Senior Member (2008)
Susanne Boll ACM Senior Member (2012)
Surendar Chandra ACM Senior Member (2009)
Kuan-Ta Chen ACM Senior Member (2015)
Matthew L Cooper ACM Senior Member (2010)
Claudio A. Feijoo ACM Senior Member (2009)
Shahram Ghandeharizadeh ACM Software System Award (2008)
Andreas Girgensohn ACM Distinguished Member (2008)
Tracy Anne Hammond ACM Senior Member (2015)
H Lynda Hardman ACM Distinguished Member (2014)
ACM Senior Member (2013)
Xian-Sheng Hua ACM Distinguished Member (2015)
ACM Senior Member (2009)
Tao Mei ACM Senior Member (2012)
Filippo Menczer ACM Distinguished Member (2013)
Saraju P. Mohanty ACM Senior Member (2010)
Nuria Oliver ACM Distinguished Member (2015)
ACM Senior Member (2013)
Ming Ouhyoung ACM Senior Member (2007)
Sethuraman Panchanathan ACM Senior Member (2009)
Yong Rui ACM Distinguished Member (2009)
ACM Senior Member (2006)
Frank Shipman ACM Distinguished Member (2009)
Matthew A Turk ACM Senior Member (2007)
Lei Zhang ACM Senior Member (2011)
Michelle Zhou ACM Distinguished Member (2009)
ACM Senior Member (2007)
Michelle Zhou ACM Distinguished Member (2009)
ACM Senior Member (2007)

First Name Last Name Paper Counts
Changsheng Xu 15
Shuicheng Yan 14
Tatseng Chua 13
Mohamed Hefeeda 13
Mohan Kankanhalli 10
Weitsang Ooi 10
Klara Nahrstedt 10
Tao Mei 7
Chenghsin Hsu 7
Gheorghita Ghinea 7
Roger Zimmermann 7
Pradeep Atrey 6
Ralf Steinmetz 6
Yong Rui 6
Svetha Venkatesh 5
Abdulmotaleb El Saddik 5
Mohammad Hossain 5
Abdulmotaleb El Saddik 5
Balakrishnan Prabhakaran 5
Hari Sundaram 5
Jitao Sang 5
Qi Tian 5
Carsten Griwodz 5
Pål Halvorsen 5
Namunu Maddage 4
Dick Bulterman 4
Xiansheng Hua 4
Zongpeng Li 4
Shipeng Li 4
Pablo César 4
Meng Wang 4
Shihfu Chang 4
Ramesh Jain 4
Oluwakemi Ademoye 4
Gabriel Muntean 4
Changwen Chen 4
Shervin Shirmohammadi 3
Prashant Shenoy 3
Marcel Worring 3
Jiangchuan Liu 3
Géraldine Morin 3
Romulus Grigoraş 3
Séamus McLoone 3
Tomás Ward 3
Zhengjun Zha 3
Richang Hong 3
Alberto Del Bimbo 3
Shueng Chan 3
Jinhui Tang 3
Rongrong Ji 3
Weiqi Yan 3
Alan Hanjalic 3
Wolfgang Effelsberg 3
Ruzena Bajcsy 3
Wenwu Zhu 3
Si Liu 3
Alexandru Iosup 3
Chuan Wu 3
Baochun Li 3
Kien Hua 3
Luming Zhang 3
Niall Murray 3
Ketan Mayer-Patel 3
Gregorij Kurillo 3
Christian Timmerer 2
Chongwah Ngo 2
Yuansong Qiao 2
Wolfgang Kellerer 2
Jie Yang 2
Jesse Jin 2
Bo Shen 2
Songqing Chen 2
Ajay Gopinathan 2
Jamesze Wang 2
Surendar Chandra 2
Keqiu Li 2
Gang Hua 2
Tianzhu Zhang 2
Azzedine Boukerche 2
Michael Needham 2
David Shamma 2
Deng Cai 2
Shervin Shirmohammadi 2
Andreas Girgensohn 2
Lynn Wilcox 2
Mark Claypool 2
Wei Cheng 2
Jia Li 2
Wuchi Feng 2
Zhi Wang 2
Rynson Lau 2
Kiana Calagari 2
Jiwu Huang 2
Sabu Emmanuel 2
Siqi Shen 2
Jiunlong Huang 2
Min Xu 2
Stephen Gulliver 2
Hai Jin 2
Sebastien Mondet 2
Peng Cui 2
Ming Yan 2
Lawrence Rowe 2
Eckehard Steinbach 2
Munchoon Chan 2
Yuru Lin 2
Xiaofei He 2
Stephan Kopf 2
Nicu Sebe 2
Wanmin Wu 2
Gerald Friedland 2
Steven Hoi 2
Houqiang Li 2
Rahul Potharaju 2
Liang Zhou 2
Hongjiang Zhang 2
Jongeun Cha 2
Min Song 2
Michael Gleicher 2
Brett Adams 2
Hendrik Knoche 2
Robert Kinicki 2
Zhenyu Yang 2
Xiangyu Wang 2
Xu Cheng 2
Shiqiang Yang 2
Vamsidhar Gaddam 2
Ragnar Langseth 2
Xi Zhou 2
Ahsan Arefin 2
Susanne Boll 2
Geoff West 2
Bing Wang 2
Don Towsley 2
Indranil Gupta 2
Nabil Sarhan 2
Ramesh Jain 2
Aisling Kelliher 2
Linjun Yang 2
Lawrence Rowe 2
Michael Pearce 2
Venugopal Vasudevan 2
Mohan Kankanhalli 2
Bin Cheng 2
Xue Li 2
Yiliang Zhao 2
Hanqing Lu 2
Daniel Gatica-Perez 2
Yeongju Lee 2
Ming Li 2
Haizhou Li 2
Kasim Candan 2
Xiaobai Liu 2
Xin Zhang 2
Yifang Yin 2
Xiaoshan Yang 2
Jiebo Luo 2
Simon Moncrieff 2
Xi Shao 2
Zhenwei Zhao 2
Derek Eager 2
Shuqiao Zhao 2
Rui Yang 2
Jiwu Huang 2
Meng Wang 2
Peiyu Lin 2
João Gomes 1
Wei Liu 1
Rodrigo De Oliveira 1
Ramesh Jain 1
Khechai Sim 1
Yan Huang 1
Jungsan Lee 1
Qingzhong Liu 1
Laura Sesma-Sanchez 1
Sonia Porta 1
Reinier Van Leuken 1
Bogdan Carbunar 1
Bingbing Ni 1
Song Ci 1
Shu Shi 1
Wanmin Wu 1
Dag Johansen 1
Sigurd Ljodal 1
Hwaijung Hsu 1
Shengfeng Qin 1
Lexing Xie 1
David Hsu 1
Zheng Lu 1
Mary Buchanan 1
Walter Bender 1
Brian Smith 1
Narasimha Reddy 1
Yehia Elkhatib 1
Stefano Montanelli 1
Goffredo Haus 1
Laura Bratton 1
Ioannis Arapakis 1
Laura Natali 1
Stefan Wilk 1
Khaled Diab 1
Wojciech Matusik 1
Yadong Mu 1
Meng Wang 1
Shin'ichi Satoh 1
Gaurav Bhatnagar 1
Zibin Wang 1
Simone Bianco 1
Weiming Hu 1
Zhongfei Zhang 1
Mohamad Rabbath 1
Howard Leung 1
Lijun Yin 1
Ruchira Naskar 1
Hairong Liu 1
Yihan Pu 1
Bo Fu 1
Dick Epema 1
Sina Jafarpour 1
Roelof Van Zwol 1
Michael Mandel 1
Mengdi Xu 1
Mubarak Shah 1
Jacky Chan 1
David Bull 1
Jonathan Doherty 1
Stephen Davies 1
Gary Wolverton 1
Peter Phillips 1
David Ott 1
Yoonjoon Lee 1
Jirong Wen 1
Rich Edgecomb 1
Lingyu Duan 1
Anlei Dong 1
Shiping Chen 1
Joseph Peters 1
Tzicker Chiueh 1
Yoav Etsion 1
Chitra Madhwacharyula 1
Hui Li 1
Ulf Myrestam 1
Xiaofei Liao 1
Zheng Zhang 1
Gunnar Harboe 1
Nizar Sakr 1
Xiansheng Hua 1
Nishan Canagarajah 1
Norman Murray 1
Nicolas Georganas 1
Junhu Wei 1
Wentong Cai 1
Liangtien Chia 1
Byunghee Jung 1
Junehwa Song 1
Virgílio Almeida 1
Jussara Almeida 1
Robert Deng 1
Ramon Aparicio-Pardo 1
Liang Chen 1
Kartik Gopalan 1
Dror Feitelson 1
Maureen Thomas 1
Crysta Metcalf 1
Elaine Huang 1
Yong Rui 1
Hangzai Luo 1
Kuowei Wu 1
Leon Pan 1
Waipun Yiu 1
Yosiyuki Takahasi 1
Thanassis Rikakis 1
Joonwon Lee 1
Frederic Thouin 1
Qingfang Zheng 1
Parag Agarwal 1
Alejandro Jaimes 1
Junwen Wu 1
Mohan Trivedi 1
Xing Jin 1
Javed Khan 1
Jonathan Walpole 1
Li Jie 1
Thomas Haenselmann 1
Chuohao Yeo 1
Stuart Whyte 1
Richard Han 1
Qin Lv 1
Ruifan Li 1
Carlo Fantozzi 1
Yukwong Kwok 1
Kaikai Liu 1
Feng Wang 1
Yirong Zhuang 1
Emil Petriu 1
Giang Nguyen 1
Cha Zhang 1
Fulvio Babich 1
Francesca Vatta 1
Alan Ip 1
Simone Santini 1
Liwei He 1
Phillipa Gill 1
Ram Dantu 1
Kalpathi Ramakrishnan 1
Jens Helmert 1
Boris Velichkovsky 1
Guang Yang 1
Mario Gerla 1
Sunil Prabhakar 1
James Williamson 1
Yogesh Rawat 1
Biao Zhu 1
Feng Xia 1
Laurencetianruo Yang 1
Renfei Wang 1
Xitong Li 1
Xiaolin Li 1
Matthias Baldauf 1
Jukka Nurminen 1
Jihad Alja'Am 1
Huajie Cui 1
Xin Liu 1
Austin Abrams 1
Xiangdong Zhou 1
Jonathan Foote 1
Amiel Heyde 1
Harlyn Baker 1
Mankwan Shan 1
Xiangwen Chen 1
Minghua Chen 1
Steven Seitz 1
Jin Yuan 1
Huanbo Luan 1
Yizhou Yu 1
Zhijie Shen 1
Shuang Wang 1
Elisabeth André 1
Eric Battenberg 1
Jon Crowcroft 1
Hao Fu 1
Wei Wu 1
Ioannis Ivrissimtzis 1
Ning Liu 1
Lorenzo Bovio 1
Marco Botta 1
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Dick Epema 1
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Affiliation Paper Counts
University of Canberra 1
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Universite Claude Bernard Lyon 1 1
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Adobe Systems Incorporated 1
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Sybase Inc. 1
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Google Inc. 1
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Kodak Research Laboratories 2
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DoCoMo Communications Laboratories Europe GmbH 2
VMware, Inc 2
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Telefonica 4
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Hong Kong University of Science and Technology 9
Heriot-Watt University 9
Carnegie Mellon University 9
The University of Hong Kong 9
Center for Mathematics and Computer Science - Amsterdam 9
Swiss Federal Institute of Technology, Lausanne 9
Portland State University 9
HP Labs 9
Technical University of Darmstadt 9
National University of Ireland, Maynooth 9
FX Palo Alto Laboratory 10
Aalto University 10
Universite de Toulouse 10
Yahoo Research Labs 10
Korea Advanced Institute of Science & Technology 10
University of Massachusetts Amherst 10
University of Mannheim 10
University of Southern California 11
Brunel University 12
University of Texas at Dallas 12
Motorola 13
City University of Hong Kong 13
Sun Yat-Sen University 13
Delft University of Technology 13
Curtin University of Technology, Perth 13
Zhejiang University 14
Huazhong University of Science and Technology 14
Institute of Automation Chinese Academy of Sciences 14
Nanyang Technological University 16
University of California, Berkeley 17
National Taiwan University 18
Institute for Infocomm Research, A-Star, Singapore 18
Columbia University 18
Tsinghua University 19
University of Oslo 22
University of Science and Technology of China 22
Arizona State University 24
University of Illinois at Urbana-Champaign 25
Simon Fraser University 30
Microsoft Research Asia 31
Chinese Academy of Sciences 33
University of Ottawa, Canada 34
National University of Singapore 114

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)

Volume 12 Issue 4, August 2016  Issue-in-Progress
Volume 12 Issue 3, June 2016
Volume 12 Issue 2, March 2016

Volume 12 Issue 1s, October 2015 Special Issue on Smartphone-Based Interactive Technologies, Systems, and Applications and Special Issue on Extended Best Papers from ACM Multimedia 2014
Volume 12 Issue 1, August 2015
Volume 11 Issue 4, April 2015
Volume 11 Issue 2s, February 2015 Special Issue on MMSYS 2014
Volume 11 Issue 3, January 2015

Volume 11 Issue 2, December 2014
Volume 11 Issue 1s, September 2014 Special Issue on Multiple Sensorial (MulSeMedia) Multimodal Media : Advances and Applications
Volume 11 Issue 1, August 2014
Volume 10 Issue 4, June 2014
Volume 10 Issue 3, April 2014
Volume 10 Issue 2, February 2014
Volume 10 Issue 1s, January 2014 Special issue of best papers of ACM MMSys 2013 and ACM NOSSDAV 2013

Volume 10 Issue 1, December 2013
Volume 9 Issue 1s, October 2013 Special Sections on the 20th Anniversary of ACM International Conference on Multimedia, Best Papers of ACM Multimedia 2012
Volume 9 Issue 4, August 2013
Volume 9 Issue 3, June 2013
Volume 9 Issue 2, May 2013
Volume 9 Issue 1, February 2013

Volume 8 Issue 4, November 2012
Volume 8 Issue 2S, September 2012 Special Issue on Multimedia Security
Volume 8 Issue 3s, September 2012 Special section of best papers of ACM multimedia 2011, and special section on 3D mobile multimedia
Volume 8 Issue 3, July 2012
Volume 8 Issue 2, May 2012
Volume 8 Issue 1S, February 2012 Special Issue on P2P Streaming
Volume 8 Issue 1, January 2012

Volume 7 Issue 4, November 2011
Volume 7S Issue 1, October 2011 Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Volume 7 Issue 3, August 2011
Volume 7 Issue 2, February 2011
Volume 7 Issue 1, January 2011

Volume 6 Issue 4, November 2010
Volume 6 Issue 3, August 2010
Volume 6 Issue 2, March 2010
Volume 6 Issue 1, February 2010

Volume 5 Issue 4, October 2009
Volume 5 Issue 3, August 2009

Volume 5 Issue 2, November 2008
Volume 4 Issue 4, October 2008
Volume 5 Issue 1, October 2008
Volume 4 Issue 3, August 2008
Volume 4 Issue 2, May 2008
Volume 4 Issue 1, January 2008

Volume 3 Issue 4, December 2007
Volume 3 Issue 3, August 2007
Volume 3 Issue 2, May 2007
Volume 3 Issue 1, February 2007

Volume 2 Issue 4, November 2006

Volume 2 Issue 3, August 06
Volume 2 Issue 2, May 2006
Volume 2 Issue 1, February 2006
Volume 1 Issue 4, November 2005
Volume 1 Issue 3, August 2005
Volume 1 Issue 2, May 2005
Volume 1 Issue 1, February 2005
All ACM Journals | See Full Journal Index