Proceedings ArticleDOI
Deep Blind Video Quality Assessment Based on Temporal Human Perception
Sewoong Ahn,Sanghoon Lee +1 more
- pp 619-623
TLDR
A deep learning scheme named Deep Blind Video Quality Assessment (DeepBVQA) is proposed to achieve a more accurate and reliable video quality predictor by considering various spatial and temporal cues which have not been considered before.Abstract:
The high performance video quality assessment (VQA) algorithm is a necessary skill to provide high quality video to viewers. However, since the nonlinear perception function between the distortion level of the video and the subjective quality score is not precisely defined, there are many limitations in accurately predicting the quality of the video. In this paper, we propose a deep learning scheme named Deep Blind Video Quality Assessment (DeepBVQA) to achieve a more accurate and reliable video quality predictor by considering various spatial and temporal cues which have not been considered before. We used CNN to extract the spatial cues of each video in VQA and proposed new hand-crafted features for temporal cues. Performance experiments show that performance is better than other state-of-the-art no-reference (NR) VQA models and the introduction of hand-crafted temporal features is very efficient in VQA.read more
Citations
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Proceedings ArticleDOI
Deep Local and Global Spatiotemporal Feature Aggregation for Blind Video Quality Assessment
Wei Zhou,Zhibo Chen +1 more
TL;DR: In this paper, the authors proposed an efficient VQA method named Deep SpatioTemporal video Quality assessor (DeepSTQ) to predict the perceptual quality of various distorted videos in a no-reference manner.
Proceedings ArticleDOI
A No-Reference Autoencoder Video Quality Metric
TL;DR: The No-reference Autoencoder VidEo (NAVE) quality metric is introduced, which is based on a deep au-toencoder machine learning technique, and is able to estimate the perceived video quality with a good correlation performance and a small error when compared to currently available no-reference and full-reference video quality objective metrics.
Proceedings ArticleDOI
Multi-pooled Inception Features for No-reference Video Quality Assessment.
TL;DR: This paper introduces a novel feature extraction method for no-reference video quality assessment (NR-VQA) relying on visual features extracted from multiple Inception modules of pretrained convolutional neural networks (CNN).
Proceedings ArticleDOI
Multiview Contrastive Learning for Completely Blind Video Quality Assessment of User Generated Content
TL;DR: This work presents a self-supervised multiview contrastive learning framework to learn spatio-temporal quality representations and captures the common information between frame differences and frames by treating them as a pair of views and similarly obtain the shared representations between frame Differences and optical flow.
Proceedings ArticleDOI
No-Reference Video Quality Assessment with Heterogeneous Knowledge Ensemble
TL;DR: Sissuire et al. as mentioned in this paper proposed a novel no-reference VQA (NR-VQA) method with HEterogeneous Knowledge Ensemble (HEKE), which can theoretically reach a lower infimum, and learn richer representation due to the heterogeneity.
References
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Image quality assessment: from error visibility to structural similarity
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Journal ArticleDOI
Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain
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TL;DR: The independent test results from the VQEG FR-TV Phase II tests are summarized, as well as results from eleven other subjective data sets that were used to develop the NTIA General Model.
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