Name
Abstract | ||
|---|---|---|
A general-purpose no-reference video quality assessment algorithm based on a long short-term memory (LSTM) network and a pretrained convolutional neural network (CNN) is introduced. Considering video sequences as a time series of deep features extracted with the help of a CNN, an LSTM network is trained to predict subjective quality scores. In contrast to previous methods, the resulting algorithm was trained on the recently published Konstanz Natural Video Quality Database (KoNViD-1k), which is the only publicly available database that contains sequences with authentic distortions. The results of experiments on KoNViD-1k demonstrate that the proposed method outperforms other state-of-the-art algorithms. Furthermore, these results are also confirmed using tests on the LIVE Video Quality Assessment Database, which consists of artificially distorted videos. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11760-019-01510-8 | Signal, Image and Video Processing |
Keywords | Field | DocType |
No-reference video quality assessment, Long short-term memory, Convolutional neural network | Pattern recognition,Convolutional neural network,Long short term memory,Artificial intelligence,Video quality,Mathematics | Journal |
Volume | Issue | ISSN |
13 | 8 | 1863-1703 |
Citations | PageRank | References |
2 | 0.37 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Domonkos Varga | 1 | 13 | 4.29 |
| Tamás Szirányi | 2 | 152 | 26.92 |