Blind Video Quality Assessment With Weakly Supervised Learning and Resampling Strategy

Zhang, Yu; Gao, Xinbo; He, Lihuo; He, Renjie

doi:10.1109/tcsvt.2018.2868063

Cited by 78 publications

(45 citation statements)

References 41 publications

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“…Based on these feature vectors, CNN and logistic regression were applied to predict video quality. Similarly, the algorithm of Zhang et al [40] also divided the input video into blocks, but the corre- and N is the length of the frame-level deep feature vector. Subsequently, an LSTM network is utilized to predict subjective quality scores sponding weak labels were derived by an FR-VQA metric.…”

Section: Related and Previous Workmentioning

confidence: 99%

“…Consequently, the dimension of the sequence data used to train the LSTM network is many times larger, allowing us to exploit the effectiveness of CNN-extracted features. In contrast to previous deep-learning-based architectures [15,36,40], we rely only on features extracted from a pretrained CNN. Furthermore, to the best of the authors' knowledge, this is the first deep architecture that was trained on a natural video quality database.…”

Section: Introductionmentioning

confidence: 99%

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No-reference video quality assessment via pretrained CNN and LSTM networks

Varga

Szirányi

2019

SIViP

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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.Keywords No-reference video quality assessment · Long short-term memory · Convolutional neural network B Domonkos Varga

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Section: Related and Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

No-reference video quality assessment via pretrained CNN and LSTM networks

Varga

Szirányi

2019

SIViP

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“…Besides, quality assessment can be achieved by fusion of primary features [8,19]. Recently, four deep learningbased VQA methods are proposed [15,20,55,56]. Kim et al [15] utilize CNN models to learn the spatio-temporal sensitivity maps.…”

Section: Related Work 21 Video Quality Assessmentmentioning

confidence: 99%

“…Liu et al [20] exploit the 3D-CNN model for codec classification and quality assessment of compressed videos. Zhang et al [55,56] apply the transfer learning technique with CNN for video quality assessment. However, all these methods are trained, validated, and tested on synthetically distorted videos.…”

Section: Related Work 21 Video Quality Assessmentmentioning

confidence: 99%

“…Unfortunately, the evaluation of current state-of-the-art NR-VQA methods [28,35] on these video databases shows a poor performance [10,23,31,42]. Existing deep learning-based VQA models are unfeasible in our problem since they either need the reference information [15,55,56] or only suit for compression artifacts [20]. Thus, this motivates us to propose the first deep learning-based model that is capable of predicting the quality of in-the-wild videos.…”

Section: Related Work 21 Video Quality Assessmentmentioning

confidence: 99%

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Quality Assessment of In-the-Wild Videos

Jiang

2019

Proceedings of the 27th ACM International Conference on Multimedia

261

245

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Quality assessment of in-the-wild videos is a challenging problem because of the absence of reference videos and shooting distortions. Knowledge of the human visual system can help establish methods for objective quality assessment of in-the-wild videos. In this work, we show two eminent effects of the human visual system, namely, content-dependency and temporal-memory effects, could be used for this purpose. We propose an objective no-reference video quality assessment method by integrating both effects into a deep neural network. For content-dependency, we extract features from a pre-trained image classification neural network for its inherent content-aware property. For temporal-memory effects, long-term dependencies, especially the temporal hysteresis, are integrated into the network with a gated recurrent unit and a subjectivelyinspired temporal pooling layer. To validate the performance of our method, experiments are conducted on three publicly available inthe-wild video quality assessment databases: KoNViD-1k, CVD2014, and LIVE-Qualcomm, respectively. Experimental results demonstrate that our proposed method outperforms five state-of-the-art methods by a large margin, specifically, 12.39%, 15.71%, 15.45%, and 18.09% overall performance improvements over the second-best method VBLIINDS, in terms of SROCC, KROCC, PLCC and RMSE, respectively. Moreover, the ablation study verifies the crucial role of both the content-aware features and the modeling of temporalmemory effects. The PyTorch implementation of our method is released at https://github.com/lidq92/VSFA. KEYWORDSvideo quality assessment; human visual system; content dependency; temporal-memory effects; in-the-wild videos ACM Reference Format:

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On the Development of a Classification Based Automated Motion Imagery Interpretability Prediction

Chen

Blasch

2021

Pattern Recognition. ICPR International Workshops and Challenges

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Blind Video Quality Assessment With Weakly Supervised Learning and Resampling Strategy

Cited by 78 publications

References 41 publications

No-reference video quality assessment via pretrained CNN and LSTM networks

No-reference video quality assessment via pretrained CNN and LSTM networks

Quality Assessment of In-the-Wild Videos

On the Development of a Classification Based Automated Motion Imagery Interpretability Prediction

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