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No-reference video quality assessment in the compressed domain
IEEE Transactions on Consumer Electronics, 2012In this paper, a novel no-reference video quality assessment algorithm in the compressed domain is introduced. The proposed algorithm takes into account of three key factors; the quantization parameter, the motion, and the bit allocation factor which are calculated using the information extracted from the compressed bitstream.
Xihong Lin
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No-reference video quality assessment by HEVC codec analysis
2015 Visual Communications and Image Processing (VCIP), 2015 This paper proposes a No-Reference (NR) Video Quality Assessment (VQA) method for videos subject to the distortion given by High Efficiency Video Coding (HEVC). The proposed assessment can be performed either as a Bitstream-Based (BB) method or as a Pixel-Based (PB).
Xin Huang 0004 +2 more
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No-Reference Video Quality Assessment using MPEG analysis
2013 Picture Coding Symposium (PCS), 2013 We present a method for No-Reference (NR) Video Quality Assessment (VQA) for decoded video without access to the bitstream. This is achieved by extracting and pooling features from a NR image quality assessment method used frame by frame. We also present methods to identify the video coding and estimate the video coding parameters for MPEG-2 and H.264 ...
Jacob Søgaard +2 more
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No-reference pixel based video quality assessment for HEVC decoded video
Journal of Visual Communication and Image Representation, 2017No-reference pixel based codec analysis of HEVC videos.No-reference pixel based estimation of the quantization parameter and PSNR for HEVC videos.No-reference pixel based video quality assessment of HEVC videos.Mapping from the feature space to a quality score using elastic net.Performance measured with content-independent cross-validation and across ...
Jacob Søgaard
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No-reference screen content video quality assessment
Displays, 2021Abstract How to effectively and accurately measure the degradation of media content is an important research topic in the field of image or video processing. Application scenarios such as online meetings, distance learning, and live game streaming make screen content video become a hot spot in Video Quality Assessment (VQA) research.
Teng Li 0006 +4 more
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Semantic Information Oriented No-Reference Video Quality Assessment
IEEE Signal Processing Letters, 2021In this letter, a method called Semantic Information Oriented No-Reference (SIONR) video quality assessment model is developed, which can effectively represent quality degradation of video by taking the variations of semantic information into consideration.
Wei Wu +3 more
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COME for No-Reference Video Quality Assessment
2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 2018Nowadays, the issue of objective Video Quality Assessment (VQA) has been extensively studied. In this paper, we present an effective general-purpose VQA method named COnvolutional neural network and Multi-regression based Evaluation (COME). It requires no referred lossless video and is universal for non-specific types of distortion.
Chunfeng Wang, Li Su 0003, Weigang Zhang
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No-reference video quality assessment on mobile devices
2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2013The explosive growth of video applications and services on mobile devices has made it important to assess video quality. In this paper, we propose a no-reference video quality assessment method for mobile videos. Based on the analysis on common mobile video impairments, three features (blockiness, blurriness and noise) were extracted.
Chen Chen +3 more
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No-Reference Video Quality Assessment with Heterogeneous Knowledge Ensemble
Proceedings of the 29th ACM International Conference on Multimedia, 2021Blind assessment of video quality is still challenging even in this deep learning era. The limited number of samples in existing databases is insufficient to learn a good feature extractor for video quality assessment (VQA), while manually labeling a larger database with subjective perception is very labor-intensive and time-consuming.
Jinjian Wu +4 more
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