This article develops an aggregate power consumption model for live video streaming systems, including many-to-many systems. In many-to-one streaming systems, multiple video sources (i.e., cameras and/or sensors) stream videos to a monitoring station. We model the power consumed by the video sources in the capturing, encoding, and transmission phases and then provide an overall model in terms of the main capturing and encoding parameters, including resolution, frame rate, number of reference frames, motion estimation range, and quantization. We also analyze the power consumed by the monitoring station due to receiving, decoding, and upscaling the received video streams. In addition to modeling the power consumption, we model the achieved bitrate of video encoding. PubDate: Mon, 18 Sep 2017 00:00:00 GMT
Abstract: Farouk Messaoudi, Adlen Ksentini, Gwendal Simon, Philippe Bertin
Mobile gaming is an emerging concept wherein gamers are using mobile devices, like smartphones and tablets, to play best-seller games. Compared to dedicated gaming boxes or PCs, these devices still fall short of executing newly complex 3D video games with a rich immersion. Three novel solutions, relying on cloud computing infrastructure, namely, computation offloading, cloud gaming, and client-server architecture, will represent the next generation of game engine architecture aiming at improving the gaming experience. The basis of these aforementioned solutions is the distribution of the game code over different devices (including set-top boxes, PCs, and servers). In order to know how the game code should be distributed, advanced knowledge of game engines is required. PubDate: Mon, 18 Sep 2017 00:00:00 GMT
With the advances in mobile devices and the popularity of social networks, users can share multimedia content anytime, anywhere. One of the most important types of emerging content is video, which is commonly shared on platforms such as Instagram and Facebook. User connections, which indicate whether two users are follower/followee or have the same interests, are essential to improve services and information relevant to users for many social media applications. But they are normally hidden due to users’ privacy concerns or are kept confidential by social media sites. Using user-shared content is an alternative way to discover user connections. This article proposes to use user-shared videos for connection discovery with the Bag of Feature Tagging method and proposes a distributed streaming computation framework to facilitate the analytics. PubDate: Mon, 18 Sep 2017 00:00:00 GMT
Performing detection on surveillance videos contributes significantly to the goals of safety and security. However, performing detection on unprotected surveillance video may reveal the privacy of innocent people in the video. Therefore, striking a proper balance between maintaining personal privacy while enhancing the feasibility of detection is an important issue. One promising solution to this problem is to encrypt the surveillance videos and perform detection on the encrypted videos. Most existing encrypted signal processing methods focus on still images or small data volumes; however, because videos are typically much larger, investigating how to process encrypted videos is a significant challenge. In this article, we propose an efficient motion detection and tracking scheme for encrypted H.264/AVC video bitstreams, which does not require the previous decryption on the encrypted video. PubDate: Mon, 18 Sep 2017 00:00:00 GMT
Abstract: Mohammad Motamedi, Philipp Gysel, Soheil Ghiasi
Deep Convolutional Neural Networks (DCNNs) exhibit remarkable performance in a number of pattern recognition and classification tasks. Modern DCNNs involve many millions of parameters and billions of operations. Inference using such DCNNs, if implemented as software running on an embedded processor, results in considerable execution time and energy consumption, which is prohibitive in many mobile applications. Field-programmable gate array (FPGA)-based acceleration of DCNN inference is a promising approach to improve both energy consumption and classification throughput. However, the engineering effort required for development and verification of an optimized FPGA-based architecture is significant. In this article, we present PLACID, an automated PLatform for Accelerator CreatIon for DCNNs. PubDate: Mon, 18 Sep 2017 00:00:00 GMT
With the advent and popularity of the social network, social graphs become essential to improve services and information relevance to users for many social media applications to predict follower/followee relationship, community membership, and so on. However, the social graphs could be hidden by users due to privacy concerns or kept by social media. Recently, connections discovered from user-shared images using machine-generated labels are proved to be more accessible alternatives to social graphs. But real-time discovery is difficult due to high complexity, and many applications are not possible. This article proposes an efficient computation framework for connection discovery using user-shared images, which is suitable for any image processing and computer vision techniques for connection discovery on the fly. PubDate: Tue, 29 Aug 2017 00:00:00 GMT
Online video presents new challenges to traditional caching with over a thousand-fold increase in number of assets, rapidly changing popularity of assets and much higher throughput requirements. We propose a new hierarchical filtering algorithm for caching online video HiFi. Our algorithm is designed to optimize hit rate, replacement rate and cache throughput. It has an associated implementation complexity comparable to that of LRU. Our results show that, under typical operator conditions, HiFi can increase edge cache byte hit rate by 5%--24% over an LRU policy, but more importantly can increase the RAM or memory byte hit rate by 80% to 200% and reduce the replacement rate by more than 100 times! These two factors combined can dramatically increase throughput for most caches. PubDate: Sat, 12 Aug 2017 00:00:00 GMT
Abstract: Duc-Tien Dang-Nguyen, Luca Piras, Giorgio Giacinto, Giulia Boato, Francesco G. B. DE Natale
In this article, we present a novel framework that can produce a visual description of a tourist attraction by choosing the most diverse pictures from community-contributed datasets, which describe different details of the queried location. The main strength of the proposed approach is its flexibility that permits us to filter out non-relevant images and to obtain a reliable set of diverse and relevant images by first clustering similar images according to their textual descriptions and their visual content and then extracting images from different clusters according to a measure of the user’s credibility. Clustering is based on a two-step process, where textual descriptions are used first and the clusters are then refined according to the visual features. PubDate: Sat, 12 Aug 2017 00:00:00 GMT
Abstract: Abukari M. Yakubu, Namunu C. Maddage, Pradeep K. Atrey
Cloud data centers (CDCs) are becoming a cost-effective method for processing and storage of multimedia data including images, video, and audio. Since CDCs are physically located in different jurisdictions, and are managed by external parties, data security is a growing concern. Data encryption at CDCs is commonly practiced to improve data security. However, to process the data at CDCs, data must often be decrypted, which raises issues in security. Thus, there is a growing demand for data processing techniques in encrypted domain in such an outsourced environment. In this article, we analyze encrypted domain speech content processing techniques for noise reduction. PubDate: Sat, 12 Aug 2017 00:00:00 GMT
Abstract: Fabrizio Guerrini, Nicola Adami, Sergio Benini, Alberto Piacenza, Julie Porteous, Marc Cavazza, Riccardo Leonardi
In this article, we discuss an innovative media entertainment application called Interactive Movietelling. As an offspring of Interactive Storytelling applied to movies, we propose to integrate narrative generation through artificial intelligence (AI) planning with video processing and modeling to construct filmic variants starting from the baseline content. The integration is possible thanks to content description using semantic attributes pertaining to intermediate-level concepts shared between video processing and planning levels. The output is a recombination of segments taken from the input movie performed so as to convey an alternative plot. User tests on the prototype proved how promising Interactive Movietelling might be, even if it was designed at a proof of concept level. PubDate: Sat, 12 Aug 2017 00:00:00 GMT
Abstract: Mingliang Zhou, Yongfei Zhang, Bo Li, Xupeng Lin
Rate control is a crucial consideration in high-efficiency video coding (HEVC). The estimation of model parameters is very important for coding tree unit (CTU)-level rate control, as it will significantly affect bit allocation and thus coding performance. However, the model parameters in the CTU-level rate control sometimes fails because of inadequate consideration of the correlation between model parameters and complexity characteristic. In this study, we establish a novel complexity correlation-based CTU-level rate control for HEVC. First, we formulate the model parameter estimation scheme as a multivariable estimation problem; second, based on the complexity correlation of the neighbouring CTU, an optimal direction is selected in five directions for reference CTU set selection during model parameter estimation to further improve the prediction accuracy of the complexity of the current CTU. PubDate: Sat, 12 Aug 2017 00:00:00 GMT
Abstract: Pai Chet Ng, James She, Kang Eun Jeon, Matthias Baldauf
The meeting of pervasive screens and smart devices has witnessed the birth of screen-smart device interaction (SSI), a key enabler to many novel interactive use cases. Most current surveys focus on direct human-screen interaction, and to the best of our knowledge, none have studied state-of-the-art SSI. This survey identifies three core elements of SSI and delivers a timely discussion on SSI oriented around the screen, the smart device, and the interaction modality. Two evaluation metrics (i.e., interaction latency and accuracy) have been adopted and refined to match the evaluation criterion of SSI. The bottlenecks that hinder the further advancement of the current SSI in connection with this metrics are studied. PubDate: Sat, 12 Aug 2017 00:00:00 GMT
Abstract: Pasi Fränti, Radu Mariescu-Istodor, Lahari Sengupta
Location-based games have been around already since 2000 but only recently when PokemonGo came to markets it became clear that they can reach wide popularity. In this article, we perform a literature-based analytical study of what kind of issues location-based game design faces, and how they can be solved. We study how to use and verify the location, the role of the games as exergames, use in education, and study technical and safety issues. As a case study, we present O-Mopsi game that combines physical activity with problem solving. It includes three challenges: (1) navigating to the next target, (2) deciding the order of targets, (3) physical movement. PubDate: Sat, 12 Aug 2017 00:00:00 GMT
Abstract: Hong-Bo Zhang, Bineng Zhong, Qing Lei, Ji-Xiang Du, Jialin Peng, Duansheng Chen, Xiao Ke
Label imbalance and the insufficiency of labeled training samples are major obstacles in most methods for counting people in images or videos. In this work, a sparse representation-based semi-supervised regression method is proposed to count people in images with limited data. The basic idea is to predict the unlabeled training data, select reliable samples to expand the labeled training set, and retrain the regression model. In the algorithm, the initial regression model, which is learned from the labeled training data, is used to predict the number of people in the unlabeled training dataset. Then, the unlabeled training samples are regarded as an over-complete dictionary. PubDate: Tue, 01 Aug 2017 00:00:00 GMT
Abstract: Luciana Fujii Pontello, Pedro H. F. Holanda, Bruno Guilherme, João Paulo V. Cardoso, Olga Goussevskaia, Ana Paula Couto Da Silva
In this work, we explore the increasing demand for novel user interfaces to navigate large media collections. We implement a geometric data structure to store and retrieve item-to-item similarity information and propose a novel navigation framework that uses vector operations and real-time user feedback to direct the outcome. The framework is scalable to large media collections and is suitable for computationally constrained devices. In particular, we implement this framework in the domain of music. To evaluate the effectiveness of the navigation process, we propose an automatic evaluation framework, based on synthetic user profiles, which allows us to quickly simulate and compare navigation paths using different algorithms and datasets. PubDate: Tue, 01 Aug 2017 00:00:00 GMT
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