No-reference video quality assessment in the compressed domain
Abstract
In 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. Characteristics of human visual system are also taken into consideration. In contrast to conventional video quality assessment metrics which measure the video quality frame by frame, the video sequence is processed by this algorithm as a whole. The proposed algorithm has been tested on the H.264/AVC bitstreams in LIVE Video Quality Database. Furthermore, bitstreams encoded by JM 17.0 with different configurations are also tested to examine the generality. The experiment results show that the proposed algorithm has better performance than many conventional methods in terms of both the prediction accuracy and the computation complexity.
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Compared to prior standardized symmetric ciphers, AES provides a shield against many attacks.
“Additionally, compared to prior standardized symmetric ciphers, AES provides a shield against many attacks [34].”
From Effectiveness of crypto-transcoding for H.264/AVC and HEVC video bit-streams (2019) · cites this paper as [34]
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References 33 · Cited by 50
- Authors
- Lin, X., Ma, H., Luo, L., & Chen, Y.
- Affiliations
- Zhejiang University (CN)
- Venue
- IEEE Transactions on Consumer Electronics
- Published
- 2012-05
- Volume
- 58
- Issue
- 2
- Pages
- 505-512
- ISSN
- 0098-3063 (ISSN-L)
- OpenAlex
- W2007829235
- Cited by
- 50
- References
- 33
- Citations by year
- 2026: 2 · 2024: 3 · 2022: 2 · 2021: 2 · 2020: 3 · 2019: 6 · 2018: 5 · 2017: 3 · 2016: 7 · 2015: 2 · 2014: 9 · 2013: 6
- FWCI
- 5.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Consumer Electronics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Signal Processing › Video Coding and Compression Technologies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- no-reference video quality assessment, compressed domain, H.264/AVC, quantization parameter, motion estimation, bit allocation, human visual system
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