No-reference video quality assessment via feature learning

Xu, Jiucheng; Ye, Peng; Liu, Yong; Doermann, David

doi:10.1109/icip.2014.7025098

Cited by 72 publications

(42 citation statements)

References 16 publications

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“…Motivated by the success of CORNIA [39] NR image quality assessment method, Xu et al [37] presented an opinionunaware architecture for NR-VQA, the so-called Video CORNIA. In particular, frame-level features are extracted via unsupervised feature learning and applied a support vector regressor (SVR) to map these onto subjective quality scores.…”

Section: Related and Previous Workmentioning

confidence: 99%

“…All methods were evaluated using fivefold cross-validation with 10 random train-validationtest split, and median PLCC and SROCC values are reported as proposed in [17] and [18]. The median PLCC and SROCC values of five baseline methods (Video BLIINDS [23], VIIDEO [20], Video CORNIA [37], FC Model [17], and STFC Model [18]) were measured by Men et al in [17] and [18]. On the other hand, the results of STS-MLP [38] and STS-SVR [38] were taken from their original publication because their authors also report on median PLCC and SROCC values using fivefold cross-validation with 10 random train-validation-test split.…”

Section: Comparison With the State Of The Artmentioning

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: Comparison With the State Of The Artmentioning

confidence: 99%

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

Varga

Szirányi

2019

SIViP

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“…6, which is based on mapping framelevel features into a spatial quality score followed by temporal pooling. The authors in Ref.…”

Section: Previous Workmentioning

confidence: 99%

No-reference video quality measurement: added value of machine learning

Mocanu

Pokhrel

Garella

et al. 2015

J. Electron. Imaging

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Video quality measurement is an important component in the end-to-end video delivery chain. Video quality is, however, subjective, and thus, there will always be interobserver differences in the subjective opinion about the visual quality of the same video. Despite this, most existing works on objective quality measurement typically focus only on predicting a single score and evaluate their prediction accuracies based on how close it is to the mean opinion scores (or similar average based ratings). Clearly, such an approach ignores the underlying diversities in the subjective scoring process and, as a result, does not allow further analysis on how reliable the objective prediction is in terms of subjective variability. Consequently, the aim of this paper is to analyze this issue and present a machine-learning based solution to address it. We demonstrate the utility of our ideas by considering the practical scenario of video broadcast transmissions with focus on digital terrestrial television (DTT) and proposing a no-reference objective video quality estimator for such application. We conducted meaningful verification studies on different video content (including video clips recorded from real DTT broadcast transmissions) in order to verify the performance of the proposed solution.Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 01/02/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Accuracy (in general) Higher than RR and NR. Higher than NR; lower than FR. Lower than FR and RR.

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“…Seshadrinathan and Bovik [37] notice the temporal hysteresis effect in the subjective experiments, and propose a temporal hysteresis pooling strategy for quality assessment. The effectiveness of this strategy has been verified in [3,37,50]. We also take account of the temporal hysteresis effects.…”

Section: Temporal Modelingmentioning

confidence: 94%

Quality Assessment of In-the-Wild Videos

Jiang

2019

Proceedings of the 27th ACM International Conference on Multimedia

240

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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No-reference video quality assessment via feature learning

Cited by 72 publications

References 16 publications

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

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

No-reference video quality measurement: added value of machine learning

Quality Assessment of In-the-Wild Videos

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