Spatio-Temporal Measures Of Naturalness

Sinno, Zeina; Bovik, Alan C.

doi:10.1109/icip.2019.8803115

Cited by 18 publications

(17 citation statements)

References 19 publications

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“…These natural scene statistics (NSS) models often are predictably altered by the presence of distortions [41], although they have more limited power to characterize complex, commingled distortions. Successful picture quality models of this type have been explored in the wavelet (BIQI [42], DIIVINE [7], C-DIIVINE [43]), DCT (BLIINDS [44], BLIINDS-II [45]) and spatial domains (NIQE [33], BRISQUE [8]), and have been further extended to encompass natural bandpass space-time video statistics models [9], [46]- [48], among which the most well-known model is Video-BLIINDS [9]. Other extensions of empirical NSS include models of the joint statistics of the gradient magnitude and Laplacian of Gaussian (GM-LOG [11]), in log-derivative and log-Gabor spaces (DESIQUE [49]), as well as in the gradient domain of LAB color transforms (HI-GRADE [10]).…”

Section: A Traditional Bvqa Modelsmentioning

confidence: 99%

“…Another intriguing and more practical approach to integrating temporal features into BVQA models is to design separable spatial-temporal statistics [4], [48], [54], [55]. Spatial features can be modified to capture temporal effects within BIQA models like BRISQUE, whereby simple frame-differences or spatially displaced frame-differences are deployed [4], [48], [56], [57].…”

Section: A Traditional Bvqa Modelsmentioning

confidence: 99%

“…Prior BVQA methods accounting for temporal distortions, however, either rely on expensive motion estimation [9], [13], or underperform on UGC videos by only accounting for simple frame-difference statistics [4], [47], [48], even including complex CNN models [19], [26]. Here we attempt to exploit more general temporal scene statistics of natural videos to develop and improve BVQA models.…”

Section: Temporal Featuresmentioning

confidence: 99%

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RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content

Wang

et al. 2021

IEEE Open J. Signal Process.

Self Cite

114

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Blind or no-reference video quality assessment of user-generated content (UGC) has become a trending, challenging, heretofore unsolved problem. Accurate and efficient video quality predictors suitable for this content are thus in great demand to achieve more intelligent analysis and processing of UGC videos. Previous studies have shown that natural scene statistics and deep learning features are both sufficient to capture spatial distortions, which contribute to a significant aspect of UGC video quality issues. However, these models are either incapable or inefficient for predicting the quality of complex and diverse UGC videos in practical applications. Here we introduce an effective and efficient video quality model for UGC content, which we dub the Rapid and Accurate Video Quality Evaluator (RAPIQUE), which we show performs comparably to state-of-the-art (SOTA) models but with orders-of-magnitude faster runtime. RAPIQUE combines and leverages the advantages of both quality-aware scene statistics features and semantics-aware deep convolutional features, allowing us to design the first general and efficient spatial and temporal (space-time) bandpass statistics model for video quality modeling. Our experimental results on recent large-scale UGC video quality databases show that RAPIQUE delivers top performances on all the datasets at a considerably lower computational expense. We hope this work promotes and inspires further efforts towards practical modeling of video quality problems for potential real-time and low-latency applications. To promote public usage, an implementation of RAPIQUE has been made freely available online: https://github.com/vztu/RAPIQUE.

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Section: A Traditional Bvqa Modelsmentioning

confidence: 99%

Section: A Traditional Bvqa Modelsmentioning

confidence: 99%

Section: Temporal Featuresmentioning

confidence: 99%

See 1 more Smart Citation

RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content

Wang

et al. 2021

IEEE Open J. Signal Process.

Self Cite

114

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“…Classical VQA methods are grounded on different cues, such as structures (Wang et al 2004b(Wang et al , 2012, motion (Seshadrinathan and Bovik 2010; Manasa and Channappayya 2016), energy (Li et al 2016a), saliency (Zhang and Liu 2017;You et al 2014), gradients (Lu et al 2019), or natural video statistics (NVS) (Mittal et al 2016;Saad et al 2014;Sinno and Bovik 2019b). Besides, some VQA methods focus on the fusion of primary features (Freitas et al 2018;Li et al 2016b).…”

Section: Video Quality Assessmentmentioning

confidence: 99%

Unified Quality Assessment of in-the-Wild Videos with Mixed Datasets Training

Jiang

2021

Int J Comput Vis

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Video quality assessment (VQA) is an important problem in computer vision. The videos in computer vision applications are usually captured in the wild. We focus on automatically assessing the quality of in-the-wild videos, which is a challenging problem due to the absence of reference videos, the complexity of distortions, and the diversity of video contents. Moreover, the video contents and distortions among existing datasets are quite different, which leads to poor performance of datadriven methods in the cross-dataset evaluation setting. To improve the performance of quality assessment models, we borrow intuitions from human perception, specifically, content dependency and temporal-memory effects of human visual system. To face the cross-dataset evaluation challenge, we explore a mixed datasets training strategy for training a single VQA model with multiple datasets. The proposed unified framework explicitly includes three stages: relative quality assessor, nonlinear mapping, and dataset-specific perceptual scale alignment, to jointly predict relative quality, perceptual quality, and subjective quality. Experiments are conducted on four publicly available datasets for VQA in the wild, i.e., LIVE-VQC, LIVE-Qualcomm, KoNViD-1k, and CVD2014. The experimental results verify the effectiveness of the mixed datasets training strategy and prove the superior performance of the unified model in comparison with the state-of-the-art models. For reproducible research, we make the PyTorch implementation of our method available at https://github.com/lidq92/MDTVSFA. Keywords Content dependency • In-the-wild videos • Mixed datasets training • Temporal-memory effect • Video quality assessment Communicated by Mei Chen.

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“…Another intriguing and more practical approach is to design spatial-temporal separable statistics [15,[36][37][38]. Spatial features can be devised upon leading BIQA models such as BRISQUE, whereby temporal features are extended from simple frame-differences to the spatially displaced framedifferencing domains [15,36,39,40], which have been shown to be simple and effective temporal quality indicators.…”

Section: Related Workmentioning

confidence: 99%

A Temporal Statistics Model For UGC Video Quality Prediction

Chen

Wang

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

Self Cite

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Blind video quality assessment of user-generated content (UGC) has become a trending and challenging problem. Previous studies have shown the efficacy of natural scene statistics for capturing spatial distortions. The exploration of temporal video statistics on UGC, however, is relatively limited. Here we propose the first general, effective and efficient temporal statistics model accounting for temporal-or motion-related distortions for UGC video quality assessment, by analyzing regularities in the temporal bandpass domain. The proposed temporal model can serve as a plug-in module to boost existing no-reference video quality predictors that lack motion-relevant features. Our experimental results on recent large-scale UGC video databases show that the proposed model can significantly improve the performances of existing methods, at a very reasonable computational expense.

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Spatio-Temporal Measures Of Naturalness

Cited by 18 publications

References 19 publications

RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content

RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content

Unified Quality Assessment of in-the-Wild Videos with Mixed Datasets Training

A Temporal Statistics Model For UGC Video Quality Prediction

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