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      Unsupervised deep learning for real-time assessment of video streaming services

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          A Practical Guide to Training Restricted Boltzmann Machines

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            Training restricted Boltzmann machines using approximations to the likelihood gradient

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              Study of subjective and objective quality assessment of video.

              We present the results of a recent large-scale subjective study of video quality on a collection of videos distorted by a variety of application-relevant processes. Methods to assess the visual quality of digital videos as perceived by human observers are becoming increasingly important, due to the large number of applications that target humans as the end users of video. Owing to the many approaches to video quality assessment (VQA) that are being developed, there is a need for a diverse independent public database of distorted videos and subjective scores that is freely available. The resulting Laboratory for Image and Video Engineering (LIVE) Video Quality Database contains 150 distorted videos (obtained from ten uncompressed reference videos of natural scenes) that were created using four different commonly encountered distortion types. Each video was assessed by 38 human subjects, and the difference mean opinion scores (DMOS) were recorded. We also evaluated the performance of several state-of-the-art, publicly available full-reference VQA algorithms on the new database. A statistical evaluation of the relative performance of these algorithms is also presented. The database has a dedicated web presence that will be maintained as long as it remains relevant and the data is available online.
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                Author and article information

                Journal
                Multimedia Tools and Applications
                Multimed Tools Appl
                Springer Nature America, Inc
                1380-7501
                1573-7721
                November 2017
                May 31 2017
                November 2017
                : 76
                : 21
                : 22303-22327
                Article
                10.1007/s11042-017-4831-6
                a35afe9a-723a-4398-b191-9d9dff50ad4a
                © 2017

                http://creativecommons.org/licenses/by/4.0

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