ACM Transactions on

Multimedia Computing, Communications, and Applications (TOMM)

Latest Articles

Introduction to Special Issue on Multimedia Big Data

Toward 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... (more)

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

We consider providing large-scale Netflix-like video-on-demand (VoD) service on a cloud platform, where cloud proxy servers are placed close to user... (more)

A Deployment Optimization Scheme Over Multimedia Big Data for Large-Scale Media Streaming Application

With the prosperity of media streaming applications over the Internet in the past decades,... (more)

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... (more)

Real-Time Load Reduction in Multimedia Big Data for Mobile Internet

In the age of multimedia big data, the popularity of mobile devices has been in an unprecedented growth, the speed of data increasing is faster than... (more)

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

Software-Defined Networking (SDN) is an emerging architecture for the next-generation Internet,... (more)


[September 2016]


The 2016 ACM Transactions on Multimedia Computing, Communications and Applications (TOMM) Nicolas D. Georganas Best Paper Award is provided to the paper “Cross-Platform Emerging Topic Detection and Elaboration from Multimedia Streams” (TOMM vol. 11, Issue 4) by Bing-Kun Bao, Changsheng Xu, Weiqing Min and Mohammod Shamim Hossain. 

Dr. Cheng-Hsin Hsu has been nominated the ACM TOMM Associate Editor of the Year for 2016! Congratulations to Cheng-Hsin!

[August 2016]

Call for Nominations for TOMM Nicolas D. Georganas Best Paper Award

The Editor-in-Chief of ACM TOMM invites nominations for the ACM TOMM Nicolas D. Georganas Best Paper. Deadline for nominations of papers published in ACM TOMM from January 2015 to December 2015, is September 10th, 2016. See the cfn

[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 Extended to Oct. 31, 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
Predicting Occupation from Images by Combining Face and Body Context Information

Facial images embed age, gender, and other rich information that are implicitly related to occupation. In this work, we advocate that occupation prediction from a single facial image is a doable computer vision problem. We extract multilevel hand-crafted features associated with locality-constrained linear coding, and convolutional neural network features, as image occupation descriptors. To avoid the curse of dimensionality and overfitting, a boost strategy called multi-channel SVM is used to integrate features from face and body. Intra-class and inter-class visual variations are jointly considered in the boosting framework to further improve performance. In the evaluation, we verify effectiveness of predicting occupation from face, and demonstrate promising performance obtained by combining face and body information. More importantly, our work as the first attempt to integrate deep features into the multi-channel SVM framework, and shows significantly better performance over the state of the art.

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.

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.

Fixation Prediction through Multimodal Analysis

In this paper, we propose to predict human eye fixation through incorporating both audio and visual cues. Traditional visual attention models generally make the utmost of stimulis visual features, yet bypassing all audio information. But in the real world, we human beings not only direct our gaze according to visual saliency but also are attracted by salient audio cues. Psychological experiments show that audio has influence on visual attention, and subjects tend to be attracted by the sound sources. Therefore, we propose to fuse both audio and visual information to predict eye fixation. In our proposed framework, we first localize the moving-sounding objects through multimodal analysis and generate an audio attention map. Then we calculate the spatial and temporal attention maps using the visual modality. Finally, the audio, spatial and temporal attention maps are fused to generate the final audio-visual saliency map. The proposed method is applicable to scenes containing moving-sounding objects. We gather a set of video sequences and collect eye-tracking data under an audio-visual test condition. Experiment results show that we can achieve better eye fixation prediction performance when taking both audio and visual cues into consideration, especially in some typical scenes where the object motion and the audio are highly correlated.

Generalized Deep Transfer Networks for Knowledge Propagation in Heterogeneous Domains

In recent years, deep networks have been successfully applied to model image concepts and achieved competitive performance on many tasks. In spite of impressive performance, the conventional deep networks can be subjected to the decayed performance if there are insufficient training data. This problem becomes extremely severe for deep networks with powerful representation structures, making them prone to over fitting by capturing nonessential or noisy information in a small data set. Towards this end, we propose a novel generalized deep transfer networks framework, capable of transferring labeling information across heterogeneous domains, such as from text domain to image domain. The proposed framework enables to adequately mitigate the problem of insufficient image training data by bringing in rich labels from the text domain. Specially, to share the labels between two domains, we will build weakly shared layers of features, i.e., parameter- and presentation-shared layers. It allows to represent both shared inter-domain features and domain-specific features, making this structure more flexible and powerful in capturing complex data of different domains jointly than the strongly shared layers. Experimental results on real world dataset show the competitive performance of the proposed method compared with some state-of-the-art methods.

Delivering Deep Learning At Scale and At Ease

Recently, deep learning techniques have enjoyed success in various multimedia applications, such as image classification and multi-modal data analysis. Large deep learning models are developed for learning rich representations of complex data. There are two challenges to overcome before deep learning can be widely adopted in multimedia and other applications. One is usability, namely the implementation of different models and training algorithms must be done by non-experts without much effort especially when the model is large and complex. The other is scalability, that is the deep learning system must be able to provision for a huge demand of computing resources for training large models with massive datasets. To address these two challenges, in this paper, we design a distributed deep learning platform called SINGA which has an intuitive programming model based on the common layer abstraction of deep learning models. Good scalability is achieved through flexible distributed training architecture and specific optimization techniques. SINGA runs on GPUs as well as on CPUs, and we show that it outperforms many other state-of-the-art deep learning systems. Our experience with developing and training deep learning models for real-life multimedia applications in SINGA shows that the platform is both usable and scalable.

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.

Drift Compensated Robust Watermarking Algorithm for H.265/HEVC Video Stream

It has been observed in the recent literature that the drift error due to watermarking degrades the visual quality of the embedded video. The existing drift error handling strategies for recent video standards such as H.264 may not be directly applicable for upcoming HD video standards (such as HEVC) due to different compression architecture. In this paper, a compressed domain watermarking scheme is proposed for H.265/HEVC bit stream which can handle drift error propagation both for intra and inter prediction process. Additionally, the proposed scheme shows adequate robustness against re-compression attack as well as common image processing attacks while maintaining the decent visual quality. A comprehensive set of experiments have been carried out to justify the efficacy of the proposed scheme over the existing literature.

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.

Consistent Synchronization of Action Order with Least Noticeable Delays in Multi-Player Online Games

When running multi-player online games on IP networks with losses and delays, the order of actions may be changed when compared to the order run on an ideal network with no delays and losses. To maintain a proper ordering of events, traditional approaches either use rollbacks to undo certain actions, or use local lags to introduce additional delays. Both may be perceived by players because their changes are beyond the just-noticeable-difference (JND) threshold. In this paper we propose a novel method for ensuring a strongly consistent completion order of actions, where strong consistency refers to the same completion order as well as the same interval between any completion time and the corresponding ideal reference completion time under no network delay. We find that small adjustments within the JND on the duration of an action would not be perceivable, as long as the duration is comparable to the network round-trip time (RTT). We utilize this property to control the vector of durations of actions and formulate the search of the vector as a multi-dimensional optimization problem. By using the property that players are generally more sensitive to the most prominent delay effect (with the highest probability of noticeability Pnotice, or the probability of correctly noticing a change when compared to the reference), we prove that the optimal solution occurs when Pnotice of the individual adjustments are equal. As this search can be done efficiently in polynomial time (<5 ms) with a small amount of space (<160 KB), the search can be done at run time to determine the optimal control. Lastly, we evaluate our approach on a popular open-source online shooting game BZFlag.

Introduction to Special Issue MMSys/NOSSDAV 2015

Identification of Reconstructed Speech

Both voice conversion and hidden Markov model (HMM)-based speech synthesis can be used to produce artificial voices of a target speaker. They have shown great negative impacts on speaker verification (SV) systems. In order to enhance the security of SV systems, the techniques to detect converted/synthesized speech should be taken into consideration. During voice conversion and HMM-based synthesis, speech reconstruction is applied to transform a set of acoustic parameters to reconstructed speech. Hence, the identification of reconstructed speech can be used to distinguish the converted/synthesized speech from the human speech. Several related works on such identification have been reported. The equal error rates (EERs) lower than 5% of detecting reconstructed speech have been achieved. However, through the cross-database evaluations on different speech databases, we find that the EERs of several testing cases are higher than 10%. The robustness of detection algorithms to different speech databases needs to be improved. In this paper, we propose an algorithm to identify the reconstructed speech. Three different speech databases and two different reconstruction methods are considered in our work, which has not been addressed in the reported works. The high-dimensional data visualization approach is used to analyze the effect of speech reconstruction on Mel-frequency cepstral coefficients (MFCC) of speech signals. The Gaussianmixturemodel (GMM) supervectors of MFCC are used as acoustic features. Furthermore, a set of commonly-used classification algorithms are applied to identify reconstructed speech. According to the comparison among different classification methods, linear discriminant analysis (LDA)-ensemble classifiers are chosen in our algorithm. Extensive experimental results show that the EERs lower than 1% can be achieved by the proposed algorithm in most cases, outperforming the reported state-of-the-art identification techniques.

Content-Adaptive Display Power Saving for Internet Video Applications on Mobile Devices

Backlight scaling is a technique proposed to reduce the display panel power consumption by strategically dimming the backlight. However, for mobile video applications, a computationally intensive luminance compensation step must be performed in combination with backlight scaling to maintain the perceived appearance of video frames. This step, if done by the CPU, could easily offset the power savings via backlight dimming. Furthermore, computing the backlight scaling values requires per-frame luminance information, which is typically too energy intensive to compute on mobile devices. In this paper, we propose Content-Adaptive Display (CAD) for two typical Internet mobile video applications: video streaming and real-time video communication. CAD uses the mobile device's GPU rather than the CPU to perform luminance compensation at reduced power consumption. For video streaming where video frames are available in advance, we compute the backlight scaling schedule using a more efficient dynamic programming algorithm than existing work. For real-time video communication where video frames are generated on-the-fly, we propose a greedy algorithm to determine the backlight scaling at runtime. We implement CAD in one video streaming app and one real-time video call app on the Android platform and use a Monsoon power meter to measure the real power consumption. Experiment results show that CAD can effectively produce power savings.

An Efficient Framework for Compressed Domain Watermarking in P-frames of HEVC- Encoded Video

High Efficiency Video Coding (HEVC/H.265) is the latest and most efficient video compression standard, a successor to H.264/AVC (Advanced Video Coding) that delivers the perceptual quality equivalent to H.264/AVC with up to 50% bitrate savings. Video watermarking in compressed domain has gained much attention in recent years as a promising solution to copyright protection since fully decode and re-encode of the video stream is not required for both embedding and extraction of watermark bits. We propose a robust watermarking framework with a blind extraction process for HEVC encoded video. A readable watermark sequence is embedded invisibly in P-frame for better perceptual quality. Our watermarking framework imposes security and robustness by selecting appropriate blocks using a pseudo-random key and the spatio-temporal characteristics of the compressed video. We analyze the strengths of different compressed domain features for implementing our watermarking framework. We demonstrate the utility of the proposed work with experimental results. The results show that the proposed work effectively restricts the increase in video bit rate and degradation in perceptual quality. The proposed framework is robust against different image and video processing attacks.

Audiovisual Tool for Solfège Assessment

Solfège is a general technique used in the music learning process, which involves the vocal performance of melodies, regarding the time and duration of musical sounds as specified in the music score, properly associated with the meter-mimicking performed by the hand movement. This paper presents an audiovisual approach for automatic assessment of this relevant musical study practice. The proposed system combines the gesture of meter-mimicking (video information) with the melodic transcription (audio information), where the hand movement works as a metronome, controlling the time flow (tempo) of the musical piece. Thus, the meter-mimicking is used to align the music score (ground truth) with the sung melody, allowing the assessment even in time dynamic scenarios. Audio analysis is applied to achieve the melodic transcription of the sung notes and the solfège performances are evaluated by a set of Bayesian classifiers that were generated from real evaluations done by experts listeners.

Digital Lollipop: Studying Electrical Stimulation on the Human Tongue to Simulate Taste sensations

Among the five primary senses, the sense of taste is the least explored as a form of digital media applied in Human-Computer Interface. This paper presents an experimental instrument, the Digital Taste Lollipop, to simulate the sensation of taste (gustation) digitally on humans. It utilizes electrical stimulation on the human tongue to simulate different taste sensations. The system is capable of manipulating the properties of electric currents (magnitude, frequency, and polarity) to formulate different stimuli. To evaluate the effectiveness of this method, the system was experimentally tested in two studies. The first experiment was conducted using separate regions of the human tongue to record occurrences of basic taste sensations and their intensity levels. The results indicate occurrences of sour, salty, bitter, and sweet sensations from different regions of the tongue. One of the major discoveries of this experiment was that the sweet taste emerges via an inverse-current mechanism, which deserves further research in the future. The second study was conducted to compare the natural sour taste with artificial (virtual) sour sensation and examine the possibility of effectively controlling the artificial sour taste at three intensity levels (mild, medium, and strong). The proposed method is attractive since it does not require any chemical solutions and facilitates various research opportunities in several directions including human-computer interaction, virtual reality, food and beverage, as well as medicine.


Publication Years 2005-2016
Publication Count 477
Citation Count 2563
Available for Download 476
Downloads (6 weeks) 4595
Downloads (12 Months) 27324
Downloads (cumulative) 221151
Average downloads per article 465
Average citations per article 5
First Name Last Name Award
El Saddik Abdulmotaleb ACM Distinguished Member (2010)
ACM Senior Member (2008)
Ruzena R Bajcsy ACM Distinguished Service Award (2003)
ACM AAAI Allen Newell Award (2001)
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)
Tiejun Huang ACM Senior Member (2013)
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)
Joel Jose Rodrigues ACM Senior Member (2011)
Yong Rui ACM Distinguished Member (2009)
ACM Senior Member (2006)
Prashant J Shenoy 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
Changwen Chen 5
Namunu Maddage 4
Dick Bulterman 4
Xiansheng Hua 4
Zongpeng Li 4
Shipeng Li 4
Pablo César 4
Shueng Chan 4
Meng Wang 4
Shihfu Chang 4
Ramesh Jain 4
Oluwakemi Ademoye 4
Gabriel Muntean 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
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
Shervin Shirmohammadi 3
Robert Deng 3
Chuan Wu 3
Baochun Li 3
Kien Hua 3
Niall Murray 3
Luming Zhang 3
Ketan Mayer-Patel 3
Gregorij Kurillo 3
Fuhao Zou 2
Rui Yang 2
Jiwu Huang 2
Meng Wang 2
Peiyu Lin 2
Christian Timmerer 2
Chongwah Ngo 2
Yuansong Qiao 2
Wolfgang Kellerer 2
Jie Yang 2
Jesse Jin 2
Songqing Chen 2
Bo Shen 2
Jamesze Wang 2
Azzedine Boukerche 2
Ajay Gopinathan 2
Tianzhu Zhang 2
Keqiu Li 2
Surendar Chandra 2
Gang Hua 2
Michael Needham 2
David Shamma 2
Deng Cai 2
Jinjun Chen 2
Jia Li 2
Lynn Wilcox 2
Andreas Girgensohn 2
Mark Claypool 2
Wei Cheng 2
Wuchi Feng 2
Zhi Wang 2
Rynson Lau 2
Kiana Calagari 2
Sabu Emmanuel 2
Jiwu Huang 2
Siqi Shen 2
Jiunlong Huang 2
Min Xu 2
Stephen Gulliver 2
Hai Jin 2
Sebastien Mondet 2
Ming Yan 2
Peng Cui 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
Laurencetianruo Yang 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
Vamsidhar Gaddam 2
Ragnar Langseth 2
Brett Adams 2
Hendrik Knoche 2
Robert Kinicki 2
Zhenyu Yang 2
Xiangyu Wang 2
Hefei Ling 2
Xu Cheng 2
Shiqiang Yang 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
Lawrence Rowe 2
Linjun Yang 2
Xue Li 2
Michael Pearce 2
Venugopal Vasudevan 2
Mohan Kankanhalli 2
Bin Cheng 2
Yiliang Zhao 2
Hanqing Lu 2
Daniel Gatica-Perez 2
Yeongju Lee 2
Ming Li 2
Haizhou Li 2
Kasim Candan 2
Xiaoshan Yang 2
James She 2
Xiaobai Liu 2
Xin Zhang 2
Yifang Yin 2
Jiebo Luo 2
Simon Moncrieff 2
Xi Shao 2
Zhenwei Zhao 2
Derek Eager 2
Shuqiao Zhao 2
Zheng Yan 2
Hairong Liu 1
Dick Epema 1
Yihan Pu 1
Bo Fu 1
Sina Jafarpour 1
Roelof Van Zwol 1
Michael Mandel 1
Mengdi Xu 1
Mubarak Shah 1
Jacky Chan 1
Jonathan Doherty 1
Stephen Davies 1
David Bull 1
Gary Wolverton 1
Peter Phillips 1
David Ott 1
Jirong Wen 1
Yoonjoon Lee 1
Lingyu Duan 1
Rich Edgecomb 1
Shiping Chen 1
Anlei Dong 1
Joseph Peters 1
Tzicker Chiueh 1
Chitra Madhwacharyula 1
Yoav Etsion 1
Hui Li 1
Ulf Myrestam 1
Xiaofei Liao 1
Zheng Zhang 1
Gunnar Harboe 1
Nizar Sakr 1
Emil Petriu 1
Taotao Wu 1
Geyong Min 1
Wang Miao 1
Haozhe Wang 1
Chenhan Xu 1
Cha Zhang 1
Liwei He 1
Giang Nguyen 1
Alan Ip 1
Fulvio Babich 1
Francesca Vatta 1
Simone Santini 1
Phillipa Gill 1
Ram Dantu 1
Boris Velichkovsky 1
Kalpathi Ramakrishnan 1
Jens Helmert 1
Guang Yang 1
Mario Gerla 1
Sunil Prabhakar 1
Shanyu Tang 1
Shivendra Shivani 1
Tommy Nilsson 1
Yue Zhang 1
Zhuo Wei 1
Conghuan Ye 1
Kui Ren 1
James Williamson 1
Feng Xia 1
Yogesh Rawat 1
Biao Zhu 1
Renfei Wang 1
Xitong Li 1
Xiaolin Li 1
Matthias Baldauf 1
Jihad Alja'Am 1
Jukka Nurminen 1
Huajie Cui 1
Jonathan Foote 1
Amiel Heyde 1
Austin Abrams 1
Xiangdong Zhou 1
Mankwan Shan 1
Xin Liu 1
Harlyn Baker 1
Xiangwen Chen 1
Minghua Chen 1
Steven Seitz 1
Rachel Heck 1
Batu Truong 1
Qianni Zhang 1
Chidansh Bhatt 1
Fawaz Alsulaiman 1
Juan Silva 1
Shaojie Tang 1
Jianping Fan 1
Ragnhild Eg 1
Frank Shipman 1
Hungkhoon Tan 1
Guojun Qi 1
Anirban Mahanti 1
Wenbo He 1
Pablo Corro 1
Brian Bailey 1
Kang Li 1
Mohammad Alsmirat 1
Musab Al-Hadrusi 1
Chiapin Wang 1
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Affiliation Paper Counts
University of Surrey 1
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University of Notre Dame 1
University of Canberra 1
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Adobe Systems Incorporated 1
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Sybase Inc. 1
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MIT Media Laboratory 1
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Google Inc. 1
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Kodak Research Laboratories 2
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Telecom Research Center Vienna 2
Indian Institute of Technology, Guwahati 2
Washington University in St. Louis 2
Goldsmiths, University of London 2
Queensland University of Technology 2
RMIT University 2
Japan Advanced Institute of Science and Technology 2
University of Reading 2
University of Milan - Bicocca 2
Oldenburger Forschungs- Und Entwicklungsinstitut fur Informatik-Werkzeuge Und -Systeme 2
University of Antwerp 2
Yuan Ze University 2
University of Florida 2
National Cheng Kung University 2
University of Nottingham 2
Florida International University 2
INSA Lyon 2
Instituto de Telecomunicacoes 2
Xi'an Jiaotong University 2
Alpen-Adria-Universit¿¿t Klagenfurt 2
Binghamton University State University of New York 2
University of Oldenburg 2
Communication University of China 2
Johannes Kepler University Linz 2
University of Durham 2
Massachusetts Institute of Technology 2
Pace University 2
National Chi Nan University 2
University of Tehran 2
Nippon Telegraph & Telephone 2
University Michigan Ann Arbor 2
Roehampton University 2
Stevens Institute of Technology 2
Sharif University of Technology 2
Research Organization of Information and Systems National Institute of Informatics 2
Lund University 2
The University of Georgia 2
University of Regina 2
Texas State University-San Marcos 2
BT Group 2
Swinburne University of Technology 2
Tokyo Institute of Technology 2
International Computer Science Institute 2
University of Sciences and Technology Houari Boumediene 2
DoCoMo Communications Laboratories Europe GmbH 2
VMware, Inc 2
Indraprastha Institute of Information Technology Delhi 2
Universite Lille 2 Droit et Sante 3
University of Texas at San Antonio 3
Hebrew University of Jerusalem 3
University of Sao Paulo 3
University of Salford 3
Fudan University 3
Oregon State University 3
University of Saskatchewan 3
Institute of Acoustics Chinese Academy of Sciences 3
IBM Thomas J. Watson Research Center 3
University of Trieste 3
Singapore Management University 3
Georgia Institute of Technology 3
University of Alabama in Huntsville 3
Chongqing University of Posts and Telecommunications 3
East China Normal University 3
New Jersey Institute of Technology 3
Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences 3
Lancaster University 3
University of Technology Sydney 3
Osaka University 3
University of North Texas 3
South China University of Technology 3
Hunan University of Science and Technology 3
University of Sydney 3
University of Udine 3
University of Vienna 3
Stony Brook University 3
Universidad Autonoma de Madrid 3
University of California, Riverside 3
Queen's University Belfast 3
University of California, San Diego 3
Shandong University 3
Umea University 3
Indian Institute of Technology, Delhi 3
Nanjing University 3
Ghent University 3
Qatar Computing Research institute 3
Harbin Institute of Technology 4
George Mason University 4
University of Ulster 4
Queen Mary, University of London 4
University of Connecticut 4
Shanghai University 4
University of California, Los Angeles 4
University of California, Santa Barbara 4
Federal University of Minas Gerais 4
University of Alberta 4
Simula Research Laboratory 4
University of Beira Interior 4
Korea University 4
University of Illinois 4
China University of Geosciences, Wuhan 4
Brigham Young University 4
University of Montreal 4
Nanjing University of Science and Technology 4
Telefonica 4
The University of British Columbia 4
University of Newcastle, Australia 4
National Taipei University of Technology 4
King Saud University 4
Telecom Bretagne 4
Indian Institute of Science 4
University of Trento 4
Liaoning Normal University 4
National Tsing Hua University 4
University of Bristol 4
Texas A and M University 5
The University of North Carolina at Chapel Hill 5
University of Exeter 5
Technical University of Madrid 5
Pontificia Universidad Catolica de Chile 5
University of Milan 5
University of Toronto 5
University of Wisconsin Madison 5
University of Padua 5
McGill University 5
National Chiao Tung University Taiwan 5
University of California, Davis 5
Aristotle University of Thessaloniki 5
University of Queensland 5
Technical University of Munich 6
Technical University of Dresden 6
University of Central Florida 6
University College London 6
Dublin City University 6
Inha University, Incheon 6
Institute of Computing Technology Chinese Academy of Sciences 6
University of Colorado at Boulder 6
Shenzhen University 6
Pennsylvania State University 6
Chinese University of Hong Kong 6
Northwestern Polytechnical University China 6
University of California, Irvine 6
Purdue University 6
Nanjing University of Post and TeleCommunications 6
Wayne State University 6
Worcester Polytechnic Institute 6
Beihang University 6
Beijing University of Posts and Telecommunications 6
University of Winnipeg 6
University of Florence 6
Athlone Institute of Technology 7
Microsoft 7
Peking University 7
University of Turin 7
Hefei University of Technology 7
Microsoft Research 8
University of Calgary 8
Academia Sinica Taiwan 8
Dalian University of Technology 8
University of Amsterdam 8
Heriot-Watt University 9
Portland State University 9
Xidian University 9
National University of Ireland, Maynooth 9
Carnegie Mellon University 9
Center for Mathematics and Computer Science - Amsterdam 9
University of Cambridge 9
University at Buffalo, State University of New York 9
The University of Hong Kong 9
HP Labs 9
Swiss Federal Institute of Technology, Lausanne 9
FX Palo Alto Laboratory 10
Korea Advanced Institute of Science & Technology 10
Yahoo Research Labs 10
University of Massachusetts Amherst 10
Aalto University 10
University of Mannheim 10
Technical University of Darmstadt 10
Universite de Toulouse 10
Hong Kong University of Science and Technology 11
University of Southern California 11
Brunel University 12
Sun Yat-Sen University 13
Motorola 13
University of Texas at Dallas 13
Delft University of Technology 13
Curtin University of Technology, Perth 13
Zhejiang University 14
City University of Hong Kong 14
Institute of Automation Chinese Academy of Sciences 14
Nanyang Technological University 16
University of California, Berkeley 17
Columbia University 18
National Taiwan University 18
Huazhong University of Science and Technology 18
Tsinghua University 19
Institute for Infocomm Research, A-Star, Singapore 19
University of Science and Technology of China 22
University of Oslo 22
Arizona State University 24
University of Illinois at Urbana-Champaign 25
Simon Fraser University 30
Microsoft Research Asia 31
Chinese Academy of Sciences 34
University of Ottawa, Canada 37
National University of Singapore 114

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

Volume 12 Issue 5s, October 2016  Issue-in-Progress
Volume 12 Issue 4s, October 2016  Issue-in-Progress
Volume 12 Issue 4, August 2016
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 5 Issue 1, October 2008
Volume 4 Issue 4, 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
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