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Reduced-reference perceptual quality assessment for video streaming
2015 IEEE International Conference on Image Processing (ICIP), 2015We propose a perceptual video quality monitoring metric for streaming applications using the optical flow features. This approach is a reduced-reference pixel-based and relies only on the deviation of the optical flow of the corrupted frames. This techniques compares an optical flow descriptor from the corrupted frame against the descriptor obtained ...
Mohammed A. Aabed, Ghassan Al-Regib
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No-reference video quality assessment via feature learning
2014 IEEE International Conference on Image Processing (ICIP), 2014In this paper, we propose a novel “Opinion Free” (OF) No-Reference Video Quality Assessment (NR-VQA) algorithm based on frame-level unsupervised feature learning and hysteresis temporal pooling. The system consists of three components: feature extraction with max-min pooling, frame quality prediction and temporal pooling. Frame level features are first
Jingtao Xu +3 more
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Full-reference video quality assessment on high-definition video content
2012 6th International Conference on Signal Processing and Communication Systems, 2012The paper provided herein aims at investigating two state-of-the-art publicly available full-reference video quality assessment metrics, particularly with regard to high-definition video data. Concretely, we will concentrate on the performance of the Multi-Scale Structural Similarity index (MS-SSIM) and the NTIA General Video Quality Metric (VQM ...
Steffen Wulf, Udo Zölzer
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Learning Based Hybrid No-Reference Video Quality Assessment of Compressed Videos
2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019A near real-time no-reference video quality assessment method is proposed for videos encoded by H.264/AVC codec. A fully connected neural network is trained with features extracted from both bit-stream and pixel domains along with their respective subjective quality scores.
Yasamin Fazliani +2 more
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CVD2014—A Database for Evaluating No-Reference Video Quality Assessment Algorithms
IEEE Transactions on Image Processing, 2016In this paper, we present a new video database: CVD2014-Camera Video Database. In contrast to previous video databases, this database uses real cameras rather than introducing distortions via post-processing, which results in a complex distortion space in regard to the video acquisition process.
Virtanen, Toni +6 more
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CNN-MR for No Reference Video Quality Assessment
2017 4th International Conference on Information Science and Control Engineering (ICISCE), 2017In this paper, we propose a no-reference video quality assessment (VQA) method based on Convolutional Neural Network (CNN) and Multi-Regression (CNN-MR). It is universal for non-specific types of distortion. First, we innovatively introduce the 2D convolutional neural network into VQA model to learn the spatial quality features at frame level.
Chunfeng Wang, Li Su, Qingming Huang
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A novel no-reference video quality assessment algorithm
2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), 2018The no-reference video quality evaluation method has become a hotspot and difficulty for video quality evaluation research due to its convenience and lack of reference information. This paper proposed a spatio-temporal domain combined no-reference video quality assessment method.
Lin Yang, Yingyun Yang, Yong Ma
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Reconstruction-based no-reference video quality assessment
2016 IEEE Region 10 Conference (TENCON), 2016Video quality assessment is one of the key techniques in video communication and editing. With constraints of transmission system, storage space etc., original information of videos may not be available. No-reference video quality assessment (NRVQA) methods are in demand.
Zhenyu Wu, Hong Hu
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Analysis and Modelling of No-Reference Video Quality Assessment
2009 International Conference on Computer and Automation Engineering, 2009Video processing system may introduce some amounts of distortions in the video signal. It is crucial to measure the video quality blindly for most image processing applications which could not obtain the original image. Different Video Quality Assessment (VQA) methods are analysis in this paper.
null Yuan Tian, null Ming Zhu
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A Lightweight No-reference Video Quality Assessment Method
2023 IEEE International Conference on Visual Communications and Image Processing (VCIP), 2023Huiying Shi +3 more
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