An Efficient Method for No-Reference Video Quality Assessment

Agarla, Mirko; Celona, Luigi; Schettini, Raimondo

doi:10.3390/jimaging7030055

Cited by 12 publications

(17 citation statements)

References 40 publications

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“…Obviously, the above mentioned algorithms were evaluated exactly the same way as the proposed method which is described in Section 4.2 . Further, the performance results of eleven other methods, such as FC Model [ 45 ], STFC Model [ 45 ], STS-SVR [ 88 ], STS-MLP [ 88 ], ChipQA [ 89 ], QSA-VQM [ 50 ], Agarla et al [ 51 ], Jiang et al [ 90 ], MLSP-VQA-FF [ 6 ], MLSP-VQA-RN [ 6 ], and MLSP-VQA-HYB [ 6 ], were copied from the corresponding papers to give a more comprehensive comparison to the state-of-the-art. The results are summarized in Table 9 and Table 10 .…”

Section: Resultsmentioning

confidence: 99%

“…Based on these quality attributes, frame-level quality scores were generated and used for perceptual video quality estimation using a recurrent neural network. In [ 51 ], the authors improved further the previously mentioned method by introducing a sampling algorithm that eliminates temporal redundancy in video sequences by choosing representative video frames.…”

Section: Literature Reviewmentioning

confidence: 99%

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No-Reference Video Quality Assessment Using Multi-Pooled, Saliency Weighted Deep Features and Decision Fusion

Varga

2022

Sensors

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With the constantly growing popularity of video-based services and applications, no-reference video quality assessment (NR-VQA) has become a very hot research topic. Over the years, many different approaches have been introduced in the literature to evaluate the perceptual quality of digital videos. Due to the advent of large benchmark video quality assessment databases, deep learning has attracted a significant amount of attention in this field in recent years. This paper presents a novel, innovative deep learning-based approach for NR-VQA that relies on a set of in parallel pre-trained convolutional neural networks (CNN) to characterize versatitely the potential image and video distortions. Specifically, temporally pooled and saliency weighted video-level deep features are extracted with the help of a set of pre-trained CNNs and mapped onto perceptual quality scores independently from each other. Finally, the quality scores coming from the different regressors are fused together to obtain the perceptual quality of a given video sequence. Extensive experiments demonstrate that the proposed method sets a new state-of-the-art on two large benchmark video quality assessment databases with authentic distortions. Moreover, the presented results underline that the decision fusion of multiple deep architectures can significantly benefit NR-VQA.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

No-Reference Video Quality Assessment Using Multi-Pooled, Saliency Weighted Deep Features and Decision Fusion

Varga

2022

Sensors

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“…Comparing NR-IQA to NR-VQA models, deep learning is widely used for the former task while only a few NR-VQA deep learning models have been proposed to date [43]. Moreover, improvements are often observed when combining the distortion and contentaware features for estimating NR-IQA [37], [44] and NR-VQA [73]. Thus, we take advantage of these studies and extract these two types of features in our study.…”

Section: A Feature Extractionmentioning

confidence: 99%

“…The more severe the image distortion is, the lower the quality score will be. Recently, it was revealed that deep neural network (DNN) features are distortion-sensitive [45], [46], and NR-IQA/VQA methods began to incorporate networks for predicting distortion in their model [37], [47], [73]. Additionally, it was shown that DNN layers of increasing depth learn features of growing complexity.…”

Section: ) Distortion Featuresmentioning

confidence: 99%

“…We employ a self-supervised approach to learn a useful representation from low-cost labels in a pre-training phase. We train our distortion network with the most apparent distortion database (CSIQ) in contrast to the work of [73], where the authors used in-capture/authentic distortion data to train their model. We selected this image database because of its excellent results with Most Apparent Distortion (MAD) or what is most apparent to the human observer [50].…”

Section: ) Distortion Featuresmentioning

confidence: 99%

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No-Reference Video Quality Assessment Using Distortion Learning and Temporal Attention

Kossi¹,

Coulombe²,

Desrosiers³

et al. 2022

IEEE Access

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The rapid growth of video consumption and multimedia applications has increased the interest of the academia and industry in building tools that can evaluate perceptual video quality. Since videos might be distorted when they are captured or transmitted, it is imperative to develop reliable methods for no-reference video quality assessment (NR-VQA). To date, most NR-VQA models in prior art have been proposed for assessing a specific category of distortion, such as authentic distortions or traditional distortions. Moreover, those developed for both authentic and traditional distortions video databases have so far led to poor performances. This resulted in the reluctance of service providers to adopt multiple NR-VQA approaches, as they prefer a single algorithm capable of accurately estimating video quality in all situations. Furthermore, many existing NR-VQA methods are computationally complex and therefore impractical for various real-life applications. In this paper, we propose a novel deep learning method for NR-VQA based on multi-task learning where the distortion of individual frames in a video and the overall quality of the video are predicted by a single neural network. This enables to train the network with a greater amount and variety of data, thereby improving its performance in testing. Additionally, our method leverages temporal attention to select the frames of a video sequence which contribute the most to its perceived quality. The proposed algorithm is evaluated on five publicly-available video quality assessment (VQA) databases containing traditional and authentic distortions. Results show that our method outperforms the state-of-theart on traditional distortion databases such as LIVE VQA and CSIQ video, while also delivering competitive performance on databases containing authentic distortions such as KoNViD-1k, LIVE-Qualcomm and CVD2014.

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Video restoration based on deep learning: a comprehensive survey

et al. 2022

Self Cite

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Video restoration concerns the recovery of a clean video sequence starting from its degraded version. Different video restoration tasks exist, including denoising, deblurring, super-resolution, and reduction of compression artifacts. In this paper, we provide a comprehensive review of the main features of existing video restoration methods based on deep learning. We focus our attention on the main architectural components, strategies for motion handling, and loss functions. We analyze the standard benchmark datasets and use them to summarize the performance of video restoration methods, both in terms of effectiveness and efficiency. In conclusion, the main challenges and future research directions in video restoration using deep learning are highlighted.

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An Efficient Method for No-Reference Video Quality Assessment

Cited by 12 publications

References 40 publications

No-Reference Video Quality Assessment Using Multi-Pooled, Saliency Weighted Deep Features and Decision Fusion

No-Reference Video Quality Assessment Using Multi-Pooled, Saliency Weighted Deep Features and Decision Fusion

No-Reference Video Quality Assessment Using Distortion Learning and Temporal Attention

Video restoration based on deep learning: a comprehensive survey

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