No-Reference Video Quality Assessment Using Space-Time Chips

Ebenezer, Joshua P.; Shang, Zaixi; Wu, Yongjun; Wei, Hai; Bovik, Alan C.

doi:10.1109/mmsp48831.2020.9287151

Cited by 18 publications

(19 citation statements)

References 20 publications

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“…These databases are very challenging, and previous VQA methods, validated on synthetically distorted video datasets, do not produce quality estimates that correlate well with ground-truth Mean Opinion Scores (MOSs). For this reason, methods have been proposed that can capture both spatial and temporal distortions [ 19 , 20 ], some of which by exploiting deep learning-based techniques [ 5 , 9 , 21 ].…”

Section: Related Workmentioning

confidence: 99%

“…Finally, the AGGD parameters serve as image features and are mapped to a quality score using a SVR. The ChipQA [ 20 ] captures both spatial and temporal distortions, by building a representation of local spatiotemporal data that is attuned to local orientations of motion but is studied over large spatial fields. The quality of a video is estimated by identifying and quantifying deviations from the expected statistics of natural, undistorted space-time chips.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient Method for No-Reference Video Quality Assessment

2021

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Methods for No-Reference Video Quality Assessment (NR-VQA) of consumer-produced video content are largely investigated due to the spread of databases containing videos affected by natural distortions. In this work, we design an effective and efficient method for NR-VQA. The proposed method exploits a novel sampling module capable of selecting a predetermined number of frames from the whole video sequence on which to base the quality assessment. It encodes both the quality attributes and semantic content of video frames using two lightweight Convolutional Neural Networks (CNNs). Then, it estimates the quality score of the entire video using a Support Vector Regressor (SVR). We compare the proposed method against several relevant state-of-the-art methods using four benchmark databases containing user generated videos (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC). The results show that the proposed method at a substantially lower computational cost predicts subjective video quality in line with the state of the art methods on individual databases and generalizes better than existing methods in cross-database setup.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Efficient Method for No-Reference Video Quality Assessment

2021

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“…For video mean subtracted contrast normalized (MSCN) coefficients and spatiotemporal Gabor bandpass filtered outputs, [40] established an asymmetric generalized Gaussian distribution (AGGD) model to perceive distortions. In the meantime, optical flow [4,41], ST-chip [42], multi-scale trajectory [43], and bitstream level features [44][45][46][47] were also used to quantify distortion in video data. Although many of these methods contribute greatly to the perception of specific distortions without reference, they are not satisfactory for evaluating the quality of in-the-wild videos with sophisticated distortion.…”

Section: Related Workmentioning

confidence: 99%

Multi-Dimensional Feature Fusion Network for No-Reference Quality Assessment of In-the-Wild Videos

Jiang

Wang

et al. 2021

Sensors

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Over the past few decades, video quality assessment (VQA) has become a valuable research field. The perception of in-the-wild video quality without reference is mainly challenged by hybrid distortions with dynamic variations and the movement of the content. In order to address this barrier, we propose a no-reference video quality assessment (NR-VQA) method that adds the enhanced awareness of dynamic information to the perception of static objects. Specifically, we use convolutional networks with different dimensions to extract low-level static-dynamic fusion features for video clips and subsequently implement alignment, followed by a temporal memory module consisting of recurrent neural networks branches and fully connected (FC) branches to construct feature associations in a time series. Meanwhile, in order to simulate human visual habits, we built a parametric adaptive network structure to obtain the final score. We further validated the proposed method on four datasets (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC) to test the generalization ability. Extensive experiments have demonstrated that the proposed method not only outperforms other NR-VQA methods in terms of overall performance of mixed datasets but also achieves competitive performance in individual datasets compared to the existing state-of-the-art methods.

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“…The parameters of asymmetric generalized Gaussian distributions were considered as quality-aware features and mapped onto perceptual quality scores with the help of an SVR. Ebenezer et al [35] proposed a video representation that contains information about both the spatial and temporal information. Namely, the authors defined space-time chips as quality-aware features, which are cuts from the original video data in specific directions obtained from local motion flow.…”

Section: Related Workmentioning

confidence: 99%

No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features

Varga

2021

Electronics

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No-reference video quality assessment (NR-VQA) has piqued the scientific community’s interest throughout the last few decades, owing to its importance in human-centered interfaces. The goal of NR-VQA is to predict the perceptual quality of digital videos without any information about their distortion-free counterparts. Over the past few decades, NR-VQA has become a very popular research topic due to the spread of multimedia content and video databases. For successful video quality evaluation, creating an effective video representation from the original video is a crucial step. In this paper, we propose a powerful feature vector for NR-VQA inspired by Benford’s law. Specifically, it is demonstrated that first-digit distributions extracted from different transform domains of the video volume data are quality-aware features and can be effectively mapped onto perceptual quality scores. Extensive experiments were carried out on two large, authentically distorted VQA benchmark databases.

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No-Reference Video Quality Assessment Using Space-Time Chips

Cited by 18 publications

References 20 publications

An Efficient Method for No-Reference Video Quality Assessment

An Efficient Method for No-Reference Video Quality Assessment

Multi-Dimensional Feature Fusion Network for No-Reference Quality Assessment of In-the-Wild Videos

No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features

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