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020 _a9783030982492
024 7 _a10.1007/978-3-030-98249-2
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aGV1469.15
_b2022 EB
100 1 _aZadtootaghaj, Saman
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9685567
245 1 0 _aQuality of Experience Modeling for Cloud Gaming Services
_cby Saman Zadtootaghaj
250 _a1st edition 2022
264 1 _aCham
_bSpringer International Publishing
_c2022
300 _a1 recurso en línea (XVII, 170 páginas)
_b55 ilustraciones, 49 ilustraciones a color
336 _atexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
490 1 _aT-Labs Series in Telecommunication Services
_x2192-2829
505 0 _aIntroduction -- Gaming Quality of Experience -- Process for Model Development -- Video Coding Impairment Models -- Integration of Impairment Factors to Gaming QoE -- Performance Evaluation 95 -- Conclusion.
520 _aThis book presents the development of a gaming quality model to predict the gaming Quality of Experience (QoE) of players that could be used for planning the network service or quality monitoring of cloud gaming services. The author presents a model that is developed following a modular structure approach that keeps the different types of impairments separately. The book shows how such a modular structure allows developing a sustainable model as each component can be updated by advances in that specific research area or technology. The presented gaming quality model takes into account two modules of video quality and input quality. The latter considers the interactivity aspects of gaming. The video quality module offers a series of models that differ depending on the level of access to the video stream information, allowing high flexibility for service providers regarding the positions of measuring points within their system. In summary, the present book focuses on (1) creation of multiple image/video and cloud gaming quality datasets, (2) development of a gaming video classification, and (3) development of a series of gaming QoE models to predict the gaming QoE depending on the level of access to the video stream information. Introduces a gaming Quality of Experience (QoE) model that can be used to predict the quality of cloud gaming services; Describes multiple video quality models, signal-based, bitstream-based and planning-based models for gaming content; Presents guidelines for conducting the subjective test for assessment of gaming quality for cloud/online gaming.
988 _aSpringer_Engineering_2022
650 7 _2embne
_9141365
_aVideojuegos
650 7 _2embne
_9666069
_aInformática en la nube
776 0 8 _iPrinted edition:
_z9783030982485
776 0 8 _iPrinted edition:
_z9783030982508
776 0 8 _iPrinted edition:
_z9783030982515
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-98249-2
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _b12/2022
_dz
_eIG
_zSI