Modular Framework and Instances of Pixel-Based Video Quality Models for UHD-1/4K
IEEE Access, 2021 The popularity of video on-demand streaming services increased tremendously over the last years. Most services use http-based adaptive video streaming methods.
Steve Goring +3 more
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No-Reference Video Quality Assessment Using Distortion Learning and Temporal Attention
IEEE Access, 2022 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,
Koffi Kossi +3 more
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Feature‐based no‐reference video quality assessment using Extra Trees
IET Image Processing, 2022 With the emergence of social networks and improvements in the internet speed, the video data has become an ever‐increasing portion of the global internet traffic.
Hatef Otroshi‐Shahreza +2 more
doaj +1 more source
Three-Stream 3D deep CNN for no-Reference stereoscopic video quality assessment
Intelligent Systems with Applications, 2022 Convolutional Neural Networks (CNNs) have achieved great success in learning computer vision tasks, particularly 3D CNNs, for extracting spatio-temporal features from the given videos.
Hassan Imani +2 more
doaj +1 more source
No‐reference quality assessment of dynamic sports videos based on a spatiotemporal motion model
ETRI Journal, 2021 This paper proposes an approach to improve the performance of no‐reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model.
Hyoung‐Gook Kim +3 more
doaj +1 more source
Quality Feature Learning via Multi-Channel CNN and GRU for No-Reference Video Quality Assessment
IEEE Access, 2023 Nowadays, video quality assessment (VQA) plays a vital role in video-related industries to predict human perceived video quality to maintain the quality of service.
Ngai-Wing Kwong +3 more
doaj +1 more source
MRET: Multi-resolution transformer for video quality assessment
Frontiers in Signal Processing, 2023 No-reference video quality assessment (NR-VQA) for user generated content (UGC) is crucial for understanding and improving visual experience. Unlike video recognition tasks, VQA tasks are sensitive to changes in input resolution.
Junjie Ke +4 more
doaj +1 more source
No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System
Applied Sciences, 2022 Provisioning the stereoscopic 3D (S3D) video transmission services of admissible quality in a wireless environment is an immense challenge for video service providers.
Md Mehedi Hasan +4 more
doaj +1 more source
No-Reference Video Quality Assessment Using Codec Analysis [PDF]
IEEE Transactions on Circuits and Systems for Video Technology, 2015 A no-reference (NR) video quality assessment (VQA) method is presented for videos distorted by H.264/Advanced Video Coding (AVC) and MPEG-2. The assessment is performed without access to the bitstream. Instead, we analyze and estimate coefficients based on decoded pixels.
Jacob Søgaard +2 more
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Predictive No-Reference Assessment of Video Quality
CoRR, 2016 Among the various means to evaluate the quality of video streams, No-Reference (NR) methods have low computation and may be executed on thin clients. Thus, NR algorithms would be perfect candidates in cases of real-time quality assessment, automated quality control and, particularly, in adaptive mobile streaming.
Maria Torres Vega +2 more
openaire +2 more sources