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  Subjects -> COMPUTER SCIENCE (Total: 2064 journals)
    - ANIMATION AND SIMULATION (31 journals)
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    - COMPUTER SCIENCE (1196 journals)
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    - THEORY OF COMPUTING (8 journals)

COMPUTER SCIENCE (1196 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 20)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 27)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 12)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 15)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 5)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 12)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 15)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 5)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 4)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 19)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 8)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 6)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Hybrid Journal   (Followers: 29)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Informatica Malaysia     Open Access  
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Advanced Engineering Materials     Hybrid Journal   (Followers: 28)
Advanced Science Letters     Full-text available via subscription   (Followers: 10)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 7)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 19)
Advances in Computer Engineering     Open Access   (Followers: 4)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 51)
Advances in Engineering Software     Hybrid Journal   (Followers: 27)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 13)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Human-Computer Interaction     Open Access   (Followers: 20)
Advances in Materials Sciences     Open Access   (Followers: 14)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 44)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 5)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 9)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
AI EDAM     Hybrid Journal  
Air, Soil & Water Research     Open Access   (Followers: 11)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 11)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 5)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 5)
American Journal of Sensor Technology     Open Access   (Followers: 4)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 4)
Annals of Data Science     Hybrid Journal   (Followers: 12)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 1)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 12)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 11)
Applied Computer Systems     Open Access   (Followers: 2)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 33)
Applied Medical Informatics     Open Access   (Followers: 10)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5)
Applied System Innovation     Open Access  
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 142)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
arq: Architectural Research Quarterly     Hybrid Journal   (Followers: 7)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 7)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access   (Followers: 1)
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 4)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 11)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 9)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Big Data and Cognitive Computing     Open Access   (Followers: 2)
Biodiversity Information Science and Standards     Open Access  
Bioinformatics     Hybrid Journal   (Followers: 294)
Biomedical Engineering     Hybrid Journal   (Followers: 15)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 21)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 37)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 47)
British Journal of Educational Technology     Hybrid Journal   (Followers: 137)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 12)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 1)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal   (Followers: 2)
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 15)
Capturing Intelligence     Full-text available via subscription  
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
Cell Communication and Signaling     Open Access   (Followers: 2)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access   (Followers: 3)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 14)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Full-text available via subscription   (Followers: 11)
Circuits and Systems     Open Access   (Followers: 15)
Clean Air Journal     Full-text available via subscription   (Followers: 1)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Cluster Computing     Hybrid Journal   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Communication Methods and Measures     Hybrid Journal   (Followers: 12)
Communication Theory     Hybrid Journal   (Followers: 21)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Computational Physics     Full-text available via subscription   (Followers: 2)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 52)
Communications of the Association for Information Systems     Open Access   (Followers: 16)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access   (Followers: 1)
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 8)
Computación y Sistemas     Open Access  
Computation     Open Access   (Followers: 1)
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 9)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 16)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 5)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 30)
Computer     Full-text available via subscription   (Followers: 96)
Computer Aided Surgery     Open Access   (Followers: 6)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 16)
Computer Journal     Hybrid Journal   (Followers: 9)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 23)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 12)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 2)
Computer Music Journal     Hybrid Journal   (Followers: 19)
Computer Physics Communications     Hybrid Journal   (Followers: 7)

        1 2 3 4 5 6 | Last

Journal Cover
Broadcasting, IEEE Transactions on
Journal Prestige (SJR): 0.941
Citation Impact (citeScore): 5
Number of Followers: 12  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0018-9316
Published by IEEE Homepage  [191 journals]
  • IEEE Transactions on Broadcasting
    • Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • IEEE Transactions on Broadcasting information for authors
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Guest Editorial Special Issue on Quality of Experience for Advanced
           Broadcast Services
    • Authors: Maurizio Murroni;Reza Rassool;Li Song;Rafael Sotelo;
      Pages: 335 - 340
      Abstract: During the last decade, the evolution of the TV market has been remarkable. Broadcasters have been facing fresh challenges to cope with an increasing user demand for new services. With second-screen adoption and the increase of real-time news consumption via social channels, the broadcast landscape underwent a major transformation in recent years: viewers have begun to demand highly customized experiences that meet their individual needs.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • No Reference Quality Assessment of Stereo Video Based on Saliency and
           Sparsity
    • Authors: Jiachen Yang;Chunqi Ji;Bin Jiang;Wen Lu;Qinggang Meng;
      Pages: 341 - 353
      Abstract: With the popularity of video technology, stereoscopic video quality assessment (SVQA) has become increasingly important. Existing SVQA methods cannot achieve good performance because the videos' information is not fully utilized. In this paper, we consider various information in the videos together, construct a simple model to combine and analyze the diverse features, which is based on saliency and sparsity. First, we utilize the 3-D saliency map of sum map, which remains the basic information of stereoscopic video, as a valid tool to evaluate the videos' quality. Second, we use the sparse representation to decompose the sum map of 3-D saliency into coefficients, then calculate the features based on sparse coefficients to obtain the effective expression of videos' message. Next, in order to reduce the relevance between the features, we put them into stacked auto-encoder, mapping vectors to higher dimensional space, and adding the sparse restraint, then input them into support vector machine subsequently, and finally, get the quality assessment scores. Within that process, we take the advantage of saliency and sparsity to extract and simplify features. Through the later experiment, we can see the proposed method is fitting well with the subjective scores.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Study of the Subjective Visibility of Packet Loss Artifacts in Decoded
           Video Sequences
    • Authors: Jari Korhonen;
      Pages: 354 - 366
      Abstract: Packet loss is a significant cause of visual impairments in video broadcasting over packet-switched networks. There are several subjective and objective video quality assessment methods focused on the overall perception of video quality. However, less attention has been paid on the visibility of packet loss artifacts appearing in spatially and temporally limited regions of a video sequence. In this paper, we present the results of a subjective study, using a methodology where a video sequence is displayed on a touchscreen and the users tap it in the positions where they observe artifacts. We also analyze the objective features derived from those artifacts, and propose different models for combining those features into an objective metric for assessing the noticeability of the artifacts. The practical results show that the proposed metric predicts visibility of packet loss impairments with a reasonable accuracy. The proposed method can be applied for developing packetization and error recovery schemes to minimize the subjectively experienced distortion in error-prone networked video systems.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Toward a Blind Quality Metric for Temporally Distorted Streaming Video
    • Authors: Qingbo Wu;Hongliang Li;Fanman Meng;King N. Ngan;
      Pages: 367 - 378
      Abstract: With the rapid progress of mobile Internet, the streaming video service has boom over wireless networks in recent years. A smooth playback experience is crucial for the popularization of these services. However, limited by fluctuating bandwidth and various network impairments, the streaming video inevitably suffers kinds of stalling events, which significantly distorts its temporal structures and results in annoying jerky playback. In this paper, we propose an efficient quality metric to blindly evaluate the user experience for stalled streaming video without using its original sequence. Instead of requiring buffer or manifest information like existing methods, we only access to the decoded video and extract two complementary image features, i.e., global intensity and local texture, to estimate the stall number and duration. Then, by means of a straightforward and easy-to-use linear combination model, we can map the normalized stall number and duration information to a quantitative quality score. Experimental results on the publicly available LIVE-Avvasi mobile video database show that our predicted video quality is highly consistent with the user experience and outperforms many existing quality-of-experience models.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Quality of Experience for 3-D Immersive Media Streaming
    • Authors: Alexandros Doumanoglou;David Griffin;Javier Serrano;Nikolaos Zioulis;Truong Khoa Phan;David Jiménez;Dimitrios Zarpalas;Federico Alvarez;Miguel Rio;Petros Daras;
      Pages: 379 - 391
      Abstract: Recent advances in media capture and processing technologies have enabled new forms of true 3-D media content that increase the degree of user immersion. The demand for more engaging forms of entertainment means that content distributors and broadcasters need to fine-tune their delivery mechanisms over the Internet as well as develop new models for quantifying and predicting user experience of these new forms of content. In the work described in this paper, we undertake one of the first studies into the quality of experience (QoE) of real-time 3-D media content streamed to virtual reality (VR) headsets for entertainment purposes, in the context of game spectating. Our focus is on tele-immersive media that embed real users within virtual environments of interactive games. A key feature of engaging and realistic experiences in full 3-D media environments, is allowing users unrestricted viewpoints. However, this comes at the cost of increased network bandwidth and the need of limiting network effects in order to transmit a realistic, real-time representation of the participants. The visual quality of 3-D media is affected by geometry and texture parameters while the temporal aspects of smooth movement and synchronization are affected by lag introduced by network transmission effects. In this paper, we investigate varying network conditions for a set of tele-immersive media sessions produced in a range of visual quality levels. Further, we investigate user navigation issues that inhibit free viewpoint VR spectating of live 3-D media. After reporting on a study with multiple users we analyze the results and assess the overall QoE with respect to a range of visual quality and latency parameters. We propose a neural network QoE prediction model for 3-D media, constructed from a combination of visual and network parameters.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Toward the Assessment of Quality of Experience for Asymmetric Encoding in
           Immersive Media
    • Authors: Federica Battisti;Marco Carli;Patrick Le Callet;Pradip Paudyal;
      Pages: 392 - 406
      Abstract: The assessment of Quality of Experience (QoE) for stereoscopic 3-D video is a challenging task, especially in 3-D video compression and transmission applications. The focus of this contribution is the development of a QoE assessment framework, in line with the latest standardization progress in the field of QoE assessment, for understanding the visual effect of asymmetric and symmetric encoding for immersive media. Asymmetric stereoscopic video coding exploits the binocular suppression of the human vision system by representing one of the two views with a lower quality. This processing, while of limited effects on image quality, may influence the overall QoE. Many studies show that the QoE of immersive media such as 3-DTV can be thought as the combination of the perceived visual quality, the perceived depth quality, the visual fatigue, and visual discomfort. In this paper, we aim at: 1) exploiting the concept of preference of experience and protocols recently standardized for characterizing QoE; 2) conducting a case study using these standardized protocols to investigate the factors involving visual discomfort in stereoscopic video sequences with a focus on binocular rivalry; and 3) presenting the results of subjective experiments performed, by using the perceptual quality and preference of experience assessment protocols, for evaluating the impact of symmetrical, asymmetrical, and alternate coding schemes.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Evaluation of the Concept of Dynamic Adaptive Streaming of Light Field
           Video
    • Authors: Peter A. Kara;Aron Cserkaszky;Maria G. Martini;Attila Barsi;László Bokor;Tibor Balogh;
      Pages: 407 - 421
      Abstract: Light field visualization has progressed and developed significantly in the past years. At the time of this paper, light field displays are utilized in the industry and they are commercially available as well. Although their appearance on the consumer market is approaching, many potential applications of light field technology have not yet been addressed, such as video streaming. In this paper, we present our research on the dynamic adaptive streaming of light field video. In order to evaluate the presented concept of quality switching, we carried out a series of subjective tests, where test participants were shown light field videos containing stallings and switches in spatial and angular resolution.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Quality Assessment of an HDR Dual-Layer Backward-Compatible Codec Compared
           to Uncompromised SDR and HDR Solutions
    • Authors: Anne-Flore Perrin;Martin Řeřábek;Walt Husak;Touradj Ebrahimi;
      Pages: 422 - 431
      Abstract: Broadcasting high dynamic range (HDR) video has been demonstrated as largely preferred when compared to standard dynamic range (SDR), mainly due to its capability of representing more details in dark and bright regions. Additionally, progress over the last decades on creation, compression, transmission and rendering of HDR content signals a forthcoming deployment of HDR broadcasting services. Lack of a widely supported recommendation regarding bandwidth allocation for HDR compressed streams or a unique compression approach, prevent faster deployment of such services. This paper investigates the performance of a dual-layer backward-compatible compression codec, when compared to state-of-the-art HDR compression strategies, in terms of perceived quality. The evaluated system is a dual-layer compression scheme enabling the transmission of a backward-compatible SDR stream along with an HDR stream, reconstructed from the residual-based enhancement layer and SDR mapping (i.e., prediction). Comparison is made to two compression strategies realizing uncompromised SDR or HDR through the use of single-layer systems multiplexed with metadata. Metadata contains information necessary to map HDR into SDR or SDR into HDR streams. Our conclusion provides guidance regarding the compression strategy to use as well as bandwidth allocation for HDR delivery, ensuring both SDR and HDR contents with perceptually acceptable quality.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • A Review of Predictive Quality of Experience Management in Video Streaming
           Services
    • Authors: Maria Torres Vega;Cristian Perra;Filip De Turck;Antonio Liotta;
      Pages: 432 - 445
      Abstract: Satisfying the requirements of devices and users of online video streaming services is a challenging task. It requires not only managing the network quality of service but also to exert real-time control, addressing the user's quality of experience (QoE) expectations. QoE management is an end-to-end process that, due to the ever-increasing variety of video services, has become too complex for conventional “reactive” techniques. Herein, we review the most significant “predictive” QoE management methods for video streaming services, showing how different machine learning approaches may be used to perform proactive control. We pinpoint a selection of the best suited machine learning methods, highlighting advantages and limitations in specific service conditions. The review leads to lessons learned and guidelines to better address QoE requirements in complex video services.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Toward Better Statistical Validation of Machine Learning-Based Multimedia
           Quality Estimators
    • Authors: Manish Narwaria;
      Pages: 446 - 460
      Abstract: Objective assessment of multimedia quality using machine learning (ML) has been gaining popularity especially in the context of both traditional (e.g., terrestrial and satellite broadcast) and advance (such as over-the-top media services, IPTV) broadcast services. Being data-driven, these methods obviously rely on training to find the optimal model parameters. Therefore, to statistically compare and validate such ML-based quality predictors, the current approach randomly splits the given data into training and test sets and obtains a performance measure (for instance mean squared error, correlation coefficient etc.). The process is repeated a large number of times and parametric tests (e.g., t test) are then employed to statistically compare mean (or median) prediction accuracies. However, the current approach suffers from a few limitations (related to the qualitative aspects of training and testing data, the use of improper sample size for statistical testing, possibly dependent sample observations, and a lack of focus on quantifying the learning ability of the ML-based objective quality predictor) which have not been addressed in literature. Therefore, the main goal of this paper is to shed light on the said limitations both from practical and theoretical perspectives wherever applicable, and in the process propose an alternate approach to overcome some of them. As a major advantage, the proposed guidelines not only help in a theoretically more grounded statistical comparison but also provide useful insights into how well the ML-based objective quality predictors exploit data structure for learning. We demonstrate the added value of the proposed set of guidelines on standard datasets by comparing the performance of few existing ML-based quality estimators. A software implementation of the presented guidelines is also made publicly available to enable researchers and developers to test and compare different models in a repeatable manner.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Subjective Panoramic Video Quality Assessment Database for Coding
           Applications
    • Authors: Yingxue Zhang;Yingbin Wang;Feiyang Liu;Zizheng Liu;Yiming Li;Daiqin Yang;Zhenzhong Chen;
      Pages: 461 - 473
      Abstract: With the development of virtual reality, higher quality panoramic videos are in great demand to guarantee the immersive viewing experience. Therefore, quality assessment attaches much importance to correlated technologies. Considering the geometric transformation in projection and the limited resolution of head-mounted device (HMD), a modified display protocol of the high resolution sequences for the subjective rating test is proposed, in which an optimal display resolution is determined based on the geometry constraints between screen and human eyes. By sampling the videos to the optimal resolution before coding, the proposed method significantly alleviates the interference of HMD sampling while displaying, thus ensuring the reliability of subjective quality opinion in terms of video coding. Using the proposed display protocol, a subjective quality database for panoramic videos is established for video coding applications. The proposed database contains 50 distorted sequences obtained from ten raw panoramic video sequences. Distortions are introduced with the High Efficiency Video Coding compression. Each sequence is evaluated by 30 subjects on video quality, following the absolute category rating with hidden reference method. The rating scores and differential mean opinion scores (DMOSs) are recorded and included in the database. With the proposed database, several state-of-the-art objective quality assessment methods are further evaluated with correlation analysis. The database, including the video sequences, subjective rating scores and DMOS, can be used to facilitate future researches on coding applications.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • A Quality-of-Experience Database for Adaptive Video Streaming
    • Authors: Zhengfang Duanmu;Abdul Rehman;Zhou Wang;
      Pages: 474 - 487
      Abstract: The dynamic adaptive streaming over HTTP provides an inter-operable solution to overcome the volatile network conditions, but its complex characteristic brings new challenges to objective video quality-of-experience (QoE) measurement. To test the generalizability and to facilitate the wide usage of QoE measurement techniques in real-world applications, we establish a new database named Waterloo Streaming QoE Database III (SQoE-III). Unlike existing databases constructed with hand-crafted test sequences, the SQoE-III database, so far the largest and most realistic of its kind, consists of a total of 450 streaming videos created from diverse source content and diverse distortion patterns, with six adaptation algorithms of diverse characteristics under 13 representative network conditions. All streaming videos are assessed by 34 subjects, and a comprehensive evaluation is conducted on the performance of 15 objective QoE models from four categories with regards to their efficacy in predicting subjective QoE. Detailed correlation analysis and statistical hypothesis testing are carried out. The results of this paper shed light on the future development of adaptive bitrate streaming algorithm and video QoE monitoring system. The subjective database is available online at https://ece.uwaterloo.ca/~zduanmu/tbc2018qoe/.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Estimating Depth Range Required for 3-D Displays to Show Depth-Compressed
           Scenes Without Inducing Sense of Unnaturalness
    • Authors: Yasuhito Sawahata;Toshiya Morita;
      Pages: 488 - 497
      Abstract: It is often difficult to show deep 3-D scenes with sufficient quality on 3-D displays with light-ray reconstruction, such as light-field and integral 3-D displays, because their depth-reconstruction range is restricted. Scenes with substantial depth cannot be shown properly because the reconstructed images outside the range inevitably blur. Although promising methods for providing better quality 3-D visualization by contracting the scene depth to fit within the restricted depth range have been proposed, the size of the depth range required for showing deep 3-D scenes with perceptually appealing quality is not yet known. To reveal the required depth, we conducted evaluation experiments under practical viewing conditions using a 3-D-display simulator that provides binocular and motion disparities. Even with deep 3-D scenes (originally with a depth of 250 m), we found that a depth range of at least 1 m was necessary to show these scenes with sufficient quality in terms of naturalness using a nonlinear depth compression method. These results provide a design goal for future 3-D-television development, suggesting that 3-D displays can naturally show a variety of scenes that originally had substantial depths if they can reproduce a depth of 1 m.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Effects of Viewing Ultra-High-Resolution Images With Practical Viewing
           Distances on Familiar Impressions
    • Authors: Yoshiaki Shishikui;Yasuhito Sawahata;
      Pages: 498 - 507
      Abstract: This paper investigated the psychological effects of viewing ultra-high-resolution images. Many subjective evaluation experiments assessing the quality of images have been conducted. However, the psychological effects of viewing ultra-high-resolution images have not been well investigated, especially under non-standard viewing conditions. Although a higher image resolution has been reported to affect the sense of realness, more impressive factors that appeal to the general viewers have not been examined. This paper conducted subjective evaluation experiments, in which images with different resolutions using familiar subjects were presented to viewers with practical viewing distances, and their ratings of impressions were obtained. In addition, we examined the relationship between higher- and lower-order impressions. We found an enhancement of the impressions of “beautiful” or “delicious” with an increase in the resolutions of the presented images. Furthermore, the tendency of this impression enhancement was observed even when viewing it as far as four times the design viewing distance. The results of multiple regression analyses provide insight on the production and processing of ultra-high-resolution images for impression enhancement.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Blind Image Quality Estimation via Distortion Aggravation
    • Authors: Xiongkuo Min;Guangtao Zhai;Ke Gu;Yutao Liu;Xiaokang Yang;
      Pages: 508 - 517
      Abstract: Traditional blind image quality assessment (IQA) measures generally predict quality from a sole distorted image directly. In this paper, we first introduce multiple pseudo reference images (MPRIs) by further degrading the distorted image in several ways and to certain degrees, and then compare the similarities between the distorted image and the MPRIs. Via such distortion aggravation, we can have some references to compare with, i.e., the MPRIs, and utilize the full-reference IQA framework to compute the quality. Specifically, we apply four types and five levels of distortion aggravation to deal with the commonly encountered distortions. Local binary pattern features are extracted to describe the similarities between the distorted image and the MPRIs. The similarity scores are then utilized to estimate the overall quality. More similar to a specific pseudo reference image (PRI) indicates closer quality to this PRI. Owning to the availability of the created multiple PRIs, we can reduce the influence of image content, and infer the image quality more accurately and consistently. Validation is conducted on four mainstream natural scene image and screen content image quality assessment databases, and the proposed method is comparable to or outperforms the state-of-the-art blind IQA measures. The MATLAB source code of the proposed measure will be publicly available.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Efficient No-Reference Quality Assessment and Classification Model for
           Contrast Distorted Images
    • Authors: Hossein Ziaei Nafchi;Mohamed Cheriet;
      Pages: 518 - 523
      Abstract: In this paper, an efficient Minkowski distance-based metric for no-reference (NR) quality assessment of contrast distorted images is proposed. It is shown that higher orders of Minkowski distance and entropy provide accurate quality prediction for the contrast distorted images. The proposed metric performs predictions by extracting only three features from the distorted images followed by a regression analysis. Furthermore, the proposed features are able to classify type of the contrast distorted images with a high accuracy. Experimental results on four datasets CSIQ, TID2013, CCID2014, and SIQAD show that the proposed metric with a very low complexity provides better quality predictions than the state-of-the-art NR metrics. The MATLAB source code of the proposed metric is available to public at http://www.synchromedia.ca/system/files/MDM.zip.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • The Adjustment/Satisfaction Test (A/ST) for the Evaluation of
           Personalization in Broadcast Services and Its Application to Dialogue
           Enhancement
    • Authors: Matteo Torcoli;Jürgen Herre;Harald Fuchs;Jouni Paulus;Christian Uhle;
      Pages: 524 - 538
      Abstract: Media consumption in broadcasting is heading towards high degrees of content personalization also in audio thanks to next-generation audio systems. It is thus crucial to assess the benefit of personalized media delivery. To this end, the adjustment/satisfaction test was recently proposed. This is a perceptual test where subjects interact with a user-adjustable system and their adjustments and the resulting satisfaction levels are studied. Two configurations of this test paradigm are implemented and compared for the evaluation of Dialogue Enhancement (DE). This is an advanced broadcast service which enables the personalization of the relative level of the dialog and the background sounds. The test configuration closer to the final application is found to provide less noisy data and to be more conclusive about the quality of experience. For this configuration, DE is tested both in the case in which the original audio objects are readily available and in the case in which they are estimated by blind source separation. The results show that personalization is extensively used, resulting in increased user satisfaction, in both cases.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Modeling User Quality of Experience of Olfaction-Enhanced Multimedia
    • Authors: Niall Murray;Gabriel-Miro Muntean;Yuansong Qiao;Sean Brennan;Brian Lee;
      Pages: 539 - 551
      Abstract: Research on modelling user quality of experience (QoE) to date has primarily focused the combination of traditional media components; audio and video, or the individual influence of each. However, multisensory experiences have recently gained significant traction in the research community as a novel method to enhance QoE beyond what is possible with traditional media. This paper presents a model developed based on empirical data. It estimates user QoE of olfaction-enhanced multimedia. A set of 12 olfaction enhanced video clips were viewed by 84 assessors. A strong age and gender balance produced 6048 user ratings across six questions. Employing this dataset, the proposed model considers the influence of: system factors, user factors and content factors on user perceived QoE. The model is instantiated and validated. The analysis indicates that: content factors have a 10% influence on user QoE; age factors have an 11% influence; and gender factors have an 8% influence on user QoE. Also, content factors had the highest number of statistically significant influences across all of the factors evaluated. These results suggest that human, and content in addition to system factors play a key role in perceptual multimedia quality of olfaction enhanced multimedia. Further work is required to understand the remaining factors as well as the relationship between the media components has on QoE.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • QoE Assessment for IoT-Based Multi Sensorial Media Broadcasting
    • Authors: Lana Jalal;Matteo Anedda;Vlad Popescu;Maurizio Murroni;
      Pages: 552 - 560
      Abstract: One of the goals of next generation TV broadcast services is to provide realistic media contents to the users. The user's sense of reality can be reinforced by adding to conventional media multiple sensorial effects, through five-sense stimulus (i.e., taste, sight, touch, smell, and hearing). In a smart TV broadcasting context, especially in a home environment, to deliver the additional effects, customary devices (e.g., air conditioning, lights, etc.), provided of opportune smart features, have to be preferred to ad-hoc devices, often deployed in other applications as for example in gaming systems. In this context, a key issue is the interconnection among the smart TV and the customary devices that deliver the additional sensorial effects to the user. In smart home use cases, the Internet of Things (IoT) paradigm has been widely adopted to connect smart devices and this paper presents an IoT-based architecture for multi sensorial media delivery to TV users in a home entertainment scenario. In such a framework, home customary devices, act as smart objects interconnected via IoT network to the smart TV and play a role to implement additional effects to the conventional broadcast TV service. In this paper, the requirements in terms of synchronization between media and devices is analyzed and the architecture of the system is defined accordingly. Furthermore, a prototype is implemented in a real smart home scenario with real customary devices, which allowed a subjective test measurement campaign to assess the quality of experience of the users and the feasibility of the proposed multi sensorial media TV service.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Network Resource Allocation System for QoE-Aware Delivery of Media
           Services in 5G Networks
    • Authors: Angel Martin;Jon Egaña;Julián Flórez;Jon Montalbán;Igor G. Olaizola;Marco Quartulli;Roberto Viola;Mikel Zorrilla;
      Pages: 561 - 574
      Abstract: The explosion in the variety and volume of video services makes bandwidth and latency performance of networks more critical to the user experience. The media industry's response, HTTP-based Adaptive Streaming technology, offers media players the possibility to dynamically select the most appropriate bitrate according to the connectivity performance. Moving forward, the telecom industry's move is 5G. 5G aims efficiency by dynamic network optimization to make maximum use of the resources to get as high capacity and Quality of Service (QoS) as possible. These networks will be based on software defined networking (SDN) and network function virtualization (NFV) techniques, enabling self-management functions. Here, machine learning is a key technology to reach this 5G vision. On top of machine learning, SDN and NFV, this paper provides a network resource allocator system as the main contribution which enables autonomous network management aware of quality of experience (QoE). This system predicts demand to foresee the amount of network resources to be allocated and the topology setup required to cope with the traffic demand. Furthermore, the system dynamically provisions the network topology in a proactive way, while keeping the network operation within QoS ranges. To this end, the system processes signals from multiple network nodes and end-to-end QoS and QoE metrics. This paper evaluates the system for live and on-demand dynamic adaptive streaming over HTTP and high efficiency video coding services. From the experiment results, it is concluded that the system is able to scale the network topology and to address the level of resource efficiency, required by media streaming services.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • QoE-Aware Bandwidth Broker for HTTP Adaptive Streaming Flows in an
           SDN-Enabled HFC Network
    • Authors: Abdelhak Bentaleb;Ali C. Begen;Roger Zimmermann;
      Pages: 575 - 589
      Abstract: This paper proposes a software defined networking based bandwidth broker solution for improving viewer experience for any type of content delivered to any type of consumer device using HTTP adaptive streaming (HAS) in a hybrid fiber coax network. This solution is designed to meet per-session and per-group quality-of-experience objectives, to avoid common HAS culprits such as video instability, unfair and unequal quality distribution and network resource underutilization, and to scale to a large number of concurrent HAS sessions without introducing too much overhead. The mathematical framework behind our solution solves a convex optimization problem, which relies on a concave network utility maximization function. Results confirm the effectiveness of the proposed solution over the state-of-the-art bitrate adaptation and bandwidth allocation schemes.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Buffer State is Enough: Simplifying the Design of QoE-Aware HTTP Adaptive
           Video Streaming
    • Authors: Weiwei Huang;Yipeng Zhou;Xueyan Xie;Di Wu;Min Chen;Edith Ngai;
      Pages: 590 - 601
      Abstract: Recently, the prevalence of mobile devices together with the outburst of user-generated contents has fueled the tremendous growth of the Internet traffic taken by video streaming. To improve user-perceived quality-of-experience (QoE), dynamic adaptive streaming via HTTP (DASH) has been widely adopted by practical systems to make streaming smooth under limited bandwidth. However, previous DASH approaches mostly performed complicated rate adaptation based on bandwidth estimation, which has been proven to be unreliable over HTTP. In this paper, we simplify the design by only exploiting client-side buffer state information and propose a pure buffer-based DASH scheme to optimize user QoE. Our approach can not only get rid of the drawback caused by inaccurate bandwidth estimation, but also incur very limited overhead. We explicitly define an integrated user QoE model, which takes playback freezing, bitrate switch, and video quality into account, and then formulate the problem into a non-linear stochastic optimal control problem. Next, we utilize control theory to design a dynamic buffer-based controller for DASH, which determines video bitrate of each chunk to be requested and stabilize the buffer level in the meanwhile. Extensive experiments have been conducted to validate the advantages of our approach, and the results show that our approach can achieve the best performance compared with other alternative approaches.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • Quality of Experience Driven Rate Adaptation for Adaptive HTTP Streaming
    • Authors: Venkata Phani Kumar M;Sudipta Mahapatra;
      Pages: 602 - 620
      Abstract: A quality of experience (QoE) driven rate adaptation approach is proposed in this paper for adaptive HTTP streaming which jointly considers both bandwidth savings and video quality adaptation into account for the rate adjustment, beneficial to both service providers and subscribers. At each decision epoch, the adaptation algorithm accumulates for the time varying QoE of a viewer by accounting for all the impairments, namely, initial delay, quality transitions, and playback interruptions, which commonly occur during the playback of a video in adaptive streaming. Based on the possible bandwidth savings and resulting QoE variations, the algorithm decides on to adapt the bitrate dynamically and accordingly maximizes viewers' QoE. Since the proposed adaptation approach constructs an optimal path across the segments for adaptation, it also achieves QoE fairness among multiple clients in shared bandwidth environments. The experimental evaluation carried over real-world wireless network environments demonstrate that the proposed adaptation approach can maximize viewers' QoE in adaptive streaming, especially, under highly variable throughput conditions.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • 5G-QoE: QoE Modelling for Ultra-HD Video Streaming in 5G Networks
    • Authors: James Nightingale;Pablo Salva-Garcia;Jose M. Alcaraz Calero;Qi Wang;
      Pages: 621 - 634
      Abstract: Traffic on future fifth-generation (5G) mobile networks is predicted to be dominated by challenging video applications such as mobile broadcasting, remote surgery and augmented reality, demanding real-time, and ultra-high quality delivery. Two of the main expectations of 5G networks are that they will be able to handle ultra-high-definition (UHD) video streaming and that they will deliver services that meet the requirements of the end user's perceived quality by adopting quality of experience (QoE) aware network management approaches. This paper proposes a 5G-QoE framework to address the QoE modeling for UHD video flows in 5G networks. Particularly, it focuses on providing a QoE prediction model that is both sufficiently accurate and of low enough complexity to be employed as a continuous real-time indicator of the “health” of video application flows at the scale required in future 5G networks. The model has been developed and implemented as part of the EU 5G PPP SELFNET autonomic management framework, where it provides a primary indicator of the likely perceptual quality of UHD video application flows traversing a realistic multi-tenanted 5G mobile edge network testbed. The proposed 5G-QoE framework has been implemented in the 5G testbed, and the high accuracy of QoE prediction has been validated through comparing the predicted QoE values with not only subjective testing results but also empirical measurements in the testbed. As such, 5G-QoE would enable a holistic video flow self-optimisation system employing the cutting-edge Scalable H.265 video encoding to transmit UHD video applications in a QoE-aware manner.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • How can you get your idea to market first [advertisement]
    • Pages: 635 - 635
      Abstract: Advertisement, IEEE.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
  • IEEE Open Access Publishing
    • Pages: 636 - 636
      Abstract: Advertisement, IEEE.
      PubDate: June 2018
      Issue No: Vol. 64, No. 2 (2018)
       
 
 
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