Journal Information
ACM Transactions on Multimedia Computing, Communications and Applications (TOMCCAP)
http://tomccap.acm.org/
Impact Factor:
0.982
Publisher:
ACM
ISSN:
1551-6857
Viewed:
6248
Tracked:
10

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Call For Papers
The ACM Transactions on Multimedia Computing, Communications and Applications (TOMCCAP) is an ACM multidisciplinary, archival, scholarly journal in the general field of multimedia and applications, which started operating in January 2004. It is the prime ACM journal in the field. Multimedia is now a mature area, having evolved over approximately 20 years. The term “media” traditionally referred to entities such as audio, video, text, images, graphics, animation. New media will be added in the future, including virtual reality, holography, haptics, eSmell, eTaste, eThought, …. The term “multimedia” has now been accepted to mean documents composed of at least two correlated media. The correlation could be temporal, spatial or semantic. Applications now appear in many fields such as entertainment, publishing, advertising, banking, insurance, e-commerce, travel, medical, defense, training, geographical information systems, weather and many others.
 
TOMCCAP is soliciting paper submissions on all aspects of multimedia, as defined above, including systems, devices, signal processing and coding, graphics, databases, retrieval, networking and applications. Papers on single media (audio, video, animation, haptics,..), their processing, networking  and applications are also welcome. However, papers covering a single algorithm or aspect of one specific kind of media only (like e.g. image compression algorithms) are likely to be rejected.

Of special interest are papers on the following three major challenges: (i) new authoring tools that make authoring complex multimedia as easy as using a word processor or a drawing program, (ii) applications that make interactions with remote people and environments nearly the same as interactions with local people and environments and (iii) multimedia systems that make capturing, storing, finding, and using digital media an everyday occurrence in our computing environment. Papers on new integrated media, including virtual reality, haptics, holography, eSmell, eTaste, eThought are also highly encouraged.

The transactions consists primarily of research papers. This is an archival journal and it is intended that the papers will have lasting importance and value over time. In general, papers whose primary focus is on particular multimedia products will not be included.

The journal accepts publications in the three general subfields of multimedia computing, communications, and applications, each consisting of various areas of research. Note that this is not an exclusive list: other topics in these fields (computing, communications, applications) are also accepted.

    multimedia computing (research on systems support)
        I/O devices
        OS requirements
        storage systems
        multimedia data abstractions
        continuous media representations
        media coding and processing
        multimodal human-centered computing
        media content security and rights management
    multimedia communications (research on computer networks support)
        real-time protocols
        network resource allocation
        multicast and group communication protocols
        broadband multimedia
        wireless multimedia
        multimedia streaming
    multimedia applications (research on tools and applications)
        databases
        distributed collaboration
        video conferencing
        3D virtual environments and tele-presence
        content authoring
        content search
        webcasting
        multimedia-based teaching & learning
        multi-player games
        Internet television
        multimodal affective computing
Last updated by Dou Sun in 2017-05-12
Special Issues
Special Issue on Deep Learning for Mobile Multimedia
Submission Date: 2017-11-21

Deep Learning has become a crucial technology in the field of multimedia computing. It offers a powerful instrument to automatically produce highlevel abstractions of complex multimedia data, which can be exploited in a number of applications including object detection and recognition, speech-totext, media retrieval, multimodal data analysis, and so on. The availability of affordable large-scale parallel processing architectures, and the sharing of effective open-source codes implementing the basic learning algorithms, caused a rapid diffusion of deep learning methodologies within the research community, bringing to the development of a number of new technologies and applications, outperforming in most cases the results achieved by traditional machine learning technologies. In recent years, the possibility of implementing deep learning technologies on mobile devices has gained significant attention. Smartphones, but more in general any mobile component that holds some sensing and processing capability, may potentially become a smart object able to learn and act, either stand-alone or interconnected with other intelligent objects. In this context, deep learning not only can boost the performance of mobile multimedia applications availably nowadays, but could also pave the way towards more sophisticated uses of mobile devices. The path towards these exciting future scenarios, however, entangles a number of important research challenges. The fundamental deep learning technologies, including deep neural network architectures, training and inference methods, and so on, are hardly adapted to the requirements of the mobile and wireless multimedia environments. Therefore, new generations of mobile processors and chipsets will be required to support intensive and parallel computation, small footprint learning algorithms have to be developed to fit lower computation and lower power consumption requirements, new models of collaborative and distributed processing will be needed to deal with higher complexity tasks, and a number of other fundamental issues will have to be solved to ensure reliable, efficient and real-time deep learning technologies for mobile multimedia computing, communications and applications. The goal of this special issue is to seek original articles examining the state of the art, open research challenges, new solutions and applications for deep learning in mobile multimedia computing, processing and analytics. All submissions should contain substantial tutorial contents and be accessible to a general audience of researchers and practitioners. Topics of interest include, but are not limited to: - Hardware architectures for deep learning in the mobile - Deep network architectures for mobile environments - Recourse- and energy-efficient deep learning methods - Efficient inference methods for mobile multimedia deep networks - Real-time methods and applications of deep learning for mobile multimedia - Emerging applications of deep learning in mobile multimedia analysis, search, retrieval and management - Emerging applications of deep learning in self-driving cars, drones and other robotic platforms - Deep learning performance analysis in mobile multimedia
Last updated by Dou Sun in 2017-05-12
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