Multimedia Computing, Communications, and Applications (TOMM)


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ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 13 Issue 3s, July 2017

Section: Special Section on Deep Learning for Mobile Multimedia

Introduction to Special Issue on Deep Learning for Mobile Multimedia
Kaoru Ota, Minh Son Dao, Vasileios Mezaris, Francesco G.B. De Natale
Article No.: 33
DOI: 10.1145/3088340

Deep Learning for Mobile Multimedia: A Survey
Kaoru Ota, Minh Son Dao, Vasileios Mezaris, Francesco G. B. De Natale
Article No.: 34
DOI: 10.1145/3092831

Deep Learning (DL) has become a crucial technology for multimedia computing. It offers a powerful instrument to automatically produce high-level abstractions of complex multimedia data, which can be exploited in a number of applications, including...

Deep Artwork Detection and Retrieval for Automatic Context-Aware Audio Guides
Lorenzo Seidenari, Claudio Baecchi, Tiberio Uricchio, Andrea Ferracani, Marco Bertini, Alberto Del Bimbo
Article No.: 35
DOI: 10.1145/3092832

In this article, we address the problem of creating a smart audio guide that adapts to the actions and interests of museum visitors. As an autonomous agent, our guide perceives the context and is able to interact with users in an appropriate...

Enhancing Transmission Collision Detection for Distributed TDMA in Vehicular Networks
Sailesh Bharati, Hassan Aboubakr Omar, Weihua Zhuang
Article No.: 37
DOI: 10.1145/3092833

The increasing number of road accidents has led to the evolution of vehicular ad hoc networks (VANETs), which allow vehicles and roadside infrastructure to continuously broadcast safety messages, including necessary information to avoid undesired...

Spott: On-the-Spot e-Commerce for Television Using Deep Learning-Based Video Analysis Techniques
Florian Vandecasteele, Karel Vandenbroucke, Dimitri Schuurman, Steven Verstockt
Article No.: 38
DOI: 10.1145/3092834

Spott is an innovative second screen mobile multimedia application which offers viewers relevant information on objects (e.g., clothing, furniture, food) they see and like on their television screens. The application enables interaction between TV...

Section: Special Section on Deep Learning for Mobile Multimedia

Best Papers of the 2016 ACM Multimedia Systems (MMSys) Conference and Workshop on Network and Operating System Support for Digital Audio and Video (NOSSDAV) 2016
Christian Timmerer, Ali C. Begen
Article No.: 40
DOI: 10.1145/3084539

Distributed Rate Allocation in Switch-Based Multiparty Videoconferencing System
Stefano D'aronco, Sergio Mena, Pascal Frossard
Article No.: 41
DOI: 10.1145/3092835

Multiparty videoconferences, or more generally multiparty video calls, are gaining a lot of popularity as they offer a rich communication experience. These applications have, however, large requirements in terms of both network and computational...

Design and Performance Evaluation of Network-assisted Control Strategies for HTTP Adaptive Streaming
Giuseppe Cofano, Luca De Cicco, Thomas Zinner, Anh Nguyen-Ngoc, Phuoc Tran-Gia, Saverio Mascolo
Article No.: 42
DOI: 10.1145/3092836

This article investigates several network-assisted streaming approaches that rely on active cooperation between video streaming applications and the network. We build a Video Control Plane that enforces Video Quality Fairness among concurrent...

On Optimizing Adaptive Algorithms Based on Rebuffering Probability
Piotr Wisniewski, Jordi Mongay Batalla, Andrzej Beben, Piotr Krawiec, Andrzej Chydzinski
Article No.: 43
DOI: 10.1145/3092837

Traditionally, video adaptive algorithms aim to select the representation that better fits to the current download rate. In recent years, a number of new approaches appeared that take into account the buffer occupancy and the probability of video...

An SDN Architecture for Privacy-Friendly Network-Assisted DASH
Jan Willem Kleinrouweler, Sergio Cabrero, Pablo Cesar
Article No.: 44
DOI: 10.1145/3092838

Dynamic Adaptive Streaming over HTTP (DASH) is the premier technology for Internet video streaming. DASH efficiently uses existing HTTP-based delivery infrastructures implementing adaptive streaming. However, DASH traffic is bursty in nature. This...

Design and Analysis of QoE-Aware Quality Adaptation for DASH: A Spectrum-Based Approach
Cong Wang, Divyashri Bhat, Amr Rizk, Michael Zink
Article No.: 45
DOI: 10.1145/3092839

The dynamics of the application-layer-based control loop of dynamic adaptive streaming over HTTP (DASH) make video bitrate selection for DASH a difficult problem. In this work, we provide a DASH quality adaptation algorithm, named SQUAD, that is...

Cloud-Assisted Crowdsourced Livecast
Cong Zhang, Jiangchuan Liu, Haiyang Wang
Article No.: 46
DOI: 10.1145/3095755

The past two years have witnessed an explosion of a new generation of livecast services, represented by, GamingLive, and Dailymotion, to name but a few. With such a livecast service, geo-distributed Internet users...