FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3006361831> ?p ?o ?g. }

• W3006361831 endingPage "3223" @default.
• W3006361831 startingPage "3210" @default.
• W3006361831 abstract "Recently, many models have been developed to predict video Quality of Experience (QoE), yet the applicability of these models still faces significant challenges. Firstly, many models rely on features that are unique to a specific dataset and thus lack the capability to generalize. Due to the intricate interactions among these features, a unified representation that is independent of datasets with different modalities is needed. Secondly, existing models often lack the configurability to perform both classification and regression tasks. Thirdly, the sample size of the available datasets to develop these models is often very small, and the impact of limited data on the performance of QoE models has not been adequately addressed. To address these issues, in this work we develop a novel and end-to-end framework termed as DeepQoE. The proposed framework first uses a combination of deep learning techniques, such as word embedding and 3D convolutional neural network (C3D), to extract generalized features. Next, these features are combined and fed into a neural network for representation learning. A learned representation will then serve as input for classification or regression tasks. We evaluate the performance of DeepQoE with three datasets. The results show that for small datasets (e.g., WHU-MVQoE2016 and Live-Netflix Video Database), the performance of state-of-the-art machine learning algorithms is greatly improved by using the QoE representation from DeepQoE (e.g., 35.71% to 44.82%); while for the large dataset (e.g., VideoSet), our DeepQoE framework achieves significant performance improvement in comparison to the best baseline method (90.94% vs. 82.84%). In addition to the much improved performance, DeepQoE has the flexibility to fit different datasets, to learn QoE representation, and to perform both classification and regression problems. We also develop a DeepQoE based adaptive bitrate streaming (ABR) system to verify that our framework can be easily applied to multimedia communication service. The software package of the DeepQoE framework has been released to facilitate the current research on QoE." @default.
• W3006361831 created "2020-02-24" @default.
• W3006361831 creator A5019572221 @default.
• W3006361831 creator A5020194895 @default.
• W3006361831 creator A5020197760 @default.
• W3006361831 creator A5024496324 @default.
• W3006361831 creator A5041572550 @default.
• W3006361831 creator A5066787126 @default.
• W3006361831 date "2020-12-01" @default.
• W3006361831 modified "2023-09-24" @default.
• W3006361831 title "DeepQoE: A Multimodal Learning Framework for Video Quality of Experience (QoE) Prediction" @default.
• W3006361831 cites W1496659747 @default.
• W3006361831 cites W1678356000 @default.
• W3006361831 cites W1903029394 @default.
• W3006361831 cites W1984789278 @default.
• W3006361831 cites W2007004608 @default.
• W3006361831 cites W2016053056 @default.
• W3006361831 cites W2035072849 @default.
• W3006361831 cites W2043985056 @default.
• W3006361831 cites W2053157787 @default.
• W3006361831 cites W2055376552 @default.
• W3006361831 cites W2065236815 @default.
• W3006361831 cites W2072608368 @default.
• W3006361831 cites W2110424938 @default.
• W3006361831 cites W2118658901 @default.
• W3006361831 cites W2144745468 @default.
• W3006361831 cites W2145339207 @default.
• W3006361831 cites W2146659933 @default.
• W3006361831 cites W2165698076 @default.
• W3006361831 cites W2167407752 @default.
• W3006361831 cites W2175325787 @default.
• W3006361831 cites W2194775991 @default.
• W3006361831 cites W2250539671 @default.
• W3006361831 cites W2276285412 @default.
• W3006361831 cites W2535526893 @default.
• W3006361831 cites W2537533866 @default.
• W3006361831 cites W2573292679 @default.
• W3006361831 cites W2575940230 @default.
• W3006361831 cites W2600473424 @default.
• W3006361831 cites W2736492128 @default.
• W3006361831 cites W2740604066 @default.
• W3006361831 cites W2744628735 @default.
• W3006361831 cites W2760381851 @default.
• W3006361831 cites W2793889002 @default.
• W3006361831 cites W2799959769 @default.
• W3006361831 cites W2803557526 @default.
• W3006361831 cites W2955186941 @default.
• W3006361831 cites W2962739339 @default.
• W3006361831 cites W2963524571 @default.
• W3006361831 cites W2963820951 @default.
• W3006361831 cites W2964164085 @default.
• W3006361831 cites W2997214666 @default.
• W3006361831 cites W3007940350 @default.
• W3006361831 cites W3163885138 @default.
• W3006361831 cites W639708223 @default.
• W3006361831 doi "https://doi.org/10.1109/tmm.2020.2973828" @default.
• W3006361831 hasPublicationYear "2020" @default.
• W3006361831 type Work @default.
• W3006361831 sameAs 3006361831 @default.
• W3006361831 citedByCount "30" @default.
• W3006361831 countsByYear W30063618312020 @default.
• W3006361831 countsByYear W30063618312021 @default.
• W3006361831 countsByYear W30063618312022 @default.
• W3006361831 countsByYear W30063618312023 @default.
• W3006361831 crossrefType "journal-article" @default.
• W3006361831 hasAuthorship W3006361831A5019572221 @default.
• W3006361831 hasAuthorship W3006361831A5020194895 @default.
• W3006361831 hasAuthorship W3006361831A5020197760 @default.
• W3006361831 hasAuthorship W3006361831A5024496324 @default.
• W3006361831 hasAuthorship W3006361831A5041572550 @default.
• W3006361831 hasAuthorship W3006361831A5066787126 @default.
• W3006361831 hasConcept C108583219 @default.
• W3006361831 hasConcept C119857082 @default.
• W3006361831 hasConcept C124101348 @default.
• W3006361831 hasConcept C144024400 @default.
• W3006361831 hasConcept C154945302 @default.
• W3006361831 hasConcept C17744445 @default.
• W3006361831 hasConcept C199539241 @default.
• W3006361831 hasConcept C2776359362 @default.
• W3006361831 hasConcept C2779333187 @default.
• W3006361831 hasConcept C2779903281 @default.
• W3006361831 hasConcept C31258907 @default.
• W3006361831 hasConcept C36289849 @default.
• W3006361831 hasConcept C41008148 @default.
• W3006361831 hasConcept C41608201 @default.
• W3006361831 hasConcept C50644808 @default.
• W3006361831 hasConcept C5119721 @default.
• W3006361831 hasConcept C59404180 @default.
• W3006361831 hasConcept C81363708 @default.
• W3006361831 hasConcept C94625758 @default.
• W3006361831 hasConceptScore W3006361831C108583219 @default.
• W3006361831 hasConceptScore W3006361831C119857082 @default.
• W3006361831 hasConceptScore W3006361831C124101348 @default.
• W3006361831 hasConceptScore W3006361831C144024400 @default.
• W3006361831 hasConceptScore W3006361831C154945302 @default.
• W3006361831 hasConceptScore W3006361831C17744445 @default.
• W3006361831 hasConceptScore W3006361831C199539241 @default.
• W3006361831 hasConceptScore W3006361831C2776359362 @default.

• next