Back to Search
Start Over
Video quality assessment with dense features and ranking pooling
- Source :
- Neurocomputing. 457:242-253
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Benefiting with the rapid development of communication networks, effective video quality assessment (VQA) models which provide guidance for video transmission and compression technologies are highly demanded. This paper proposes a general-purpose full-reference VQA method combining DenseNet with spatial pyramid pooling and RankNet to not only extract high-level distortion representation and global spatial information of samples but also characterize the temporal correlation among frames. Firstly, the pretrained DenseNet is modified and finetuned to extract high-level features of distorBenefiting with the rapid development of communication networks, effective video quality assessment (VQA) models which provide guidance for video transmission and compression technologies are highly demanded. This paper proposes a general-purpose full-reference VQA method combining DenseNet with spatial pyramid pooling and RankNet to not only extract high-level distortion representation and global spatial information of samples but also characterize the temporal correlation among frames. Firstly, the pretrained DenseNet is modified and finetuned to extract high-level features of distorted videos. Then, spatial pyramid pooling is equipped in the DenseNet module to process flexible inputs with arbitrary spatial resolution. Thus, this kind of input which has the same spatial resolution as the original distorted video is processed by the well-trained DenseNet to generate frame-level quality, which considers the global spatial information of videos directly. Finally, learning to rank is introduced to explore the high-level temporal correlation of distorted videos by taking the RankNet as the temporal pooling function. The experimental results on two public VQA databases show that the proposed algorithm performs consistently with human visual perception.
- Subjects :
- business.industry
Computer science
Cognitive Neuroscience
Pooling
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Pattern recognition
Video quality
Computer Science Applications
Ranking
Artificial Intelligence
Distortion
Learning to rank
Pyramid (image processing)
Artificial intelligence
Representation (mathematics)
business
Spatial analysis
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 457
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........253fc6be949ea7bcd5adcf301e3f7eea