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EnglishThe accuracy of video quality metrics (VQMs) is an important issue for several applications. In this work, first we observe that the accuracy of several video quality metrics (VQMs) is strongly related to the spatial complexity index (SI) of the source. In particular, our investigation suggests that the VQMs are more likely to inaccurately predict the subjective quality of the processed video sequences derived from sources characterized by low SI. To address such a situation, we propose a machine learning based improvement for each of the VQMs considered in this work and a video quality metric fusion index (VQMFI) that jointly exploits all the VQMs considered in the study as well as spatiotemporal features to produce a better estimation of the subjective quality. Computational results demonstrate the superiority of our proposals on several datasets.
Full Reference Video Quality Measures Improvement using Neural Networks / Fotio Tiotsop, Lohic; Servetti, Antonio; Masala, Enrico. - STAMPA. - (2020), pp. 2737-2741. ((Intervento presentato al convegno IEEE 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) tenutosi a Barcelona, Spain nel May 2020.
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| Titolo: | Full Reference Video Quality Measures Improvement using Neural Networks |
| Autori: | |
| Data di pubblicazione: | 2020 |
| Abstract: | The accuracy of video quality metrics (VQMs) is an important issue for several applications. In t...his work, first we observe that the accuracy of several video quality metrics (VQMs) is strongly related to the spatial complexity index (SI) of the source. In particular, our investigation suggests that the VQMs are more likely to inaccurately predict the subjective quality of the processed video sequences derived from sources characterized by low SI. To address such a situation, we propose a machine learning based improvement for each of the VQMs considered in this work and a video quality metric fusion index (VQMFI) that jointly exploits all the VQMs considered in the study as well as spatiotemporal features to produce a better estimation of the subjective quality. Computational results demonstrate the superiority of our proposals on several datasets. |
| ISBN: | 9781509066315 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| FINAL_IEEExplore_ICASSP2020__09053739.pdf | Versione editoriale | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
| ICASSP2020_exMMSP2019_SI_TI.pdf | Versione autori, final draft post-refereeing | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2840345
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