Abstract
This paper proposes a No-Reference (NR) Video Quality Assessment (VQA) method for videos subject to the distortion given by the High Efficiency Video Coding (HEVC) scheme. The assessment is performed without access to the bitstream. The proposed analysis is based on the transform coefficients estimated from the decoded video pixels, which is used to estimate the level of quantization. The information from this analysis is exploited to assess the video quality. HEVC transform coefficients are modeled with a joint-Cauchy probability density function in the proposed method. To generate VQA features the quantization step used in the Intra coding is estimated. We map the obtained HEVC features using an Elastic Net to predict subjective video quality scores, Mean Opinion Scores (MOS). The performance is verified on a dataset consisting of HEVC coded 4 K UHD (resolution equal to 3840 x 2160) video sequences at different bitrates and spanning a wide range of content. The results show that the quality scores computed by the proposed method are highly correlated with the mean subjective assessments. (C) 2017 Elsevier Inc. All rights reserved.
Original language | English |
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Journal | Journal of Visual Communication and Image Representation |
Volume | 43 |
Pages (from-to) | 173-184 |
ISSN | 1047-3203 |
DOIs | |
Publication status | Published - 2017 |
Keywords
- Signal Processing
- Media Technology
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Elastic net
- HEVC analysis
- Machine learning
- No-reference
- Video quality assessment
- Decoding
- Learning systems
- Mathematical transformations
- Pixels
- Probability density function
- Video signal processing
- Cauchy probability density functions
- High-efficiency video coding
- No references
- Subjective video quality
- Video quality assessments (VQA)
- Quality control
- COMPUTER
- REFERENCE PSNR ESTIMATION
- ENCODED VIDEO
- STANDARD
- INFORMATION