Learning Adaptive Quantization Parameter for Consistent Quality Oriented Video Coding Article Swipe
Tien Vu Huu
,
Minh Ngoc
,
Sang Quang Nguyen
,
Huy Phi Cong
,
Thipphaphone Sisouvong
,
Xiem HoangVan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12244905
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12244905
In the industry 4.0 era, video applications such as surveillance visual systems, video conferencing, or video broadcasting have been playing a vital role. In these applications, for manipulating and tracking objects in decoded video, the quality of decoded video should be consistent because it largely affects the performance of the machine analysis. To cope with this problem, we propose a novel perceptual video coding (PVC) solution in which a full reference quality metric named video multimethod assessment fusion (VMAF) is employed together with a deep convolutional neural network (CNN) to obtain consistent quality while still achieving high compression performance. First of all, in order to achieve the consistent quality requirement, we propose a CNN model with an expected VMAF as input to adaptively adjust the quantization parameters (QP) for each coding block. Afterwards, to increase the compression performance, a Lagrange coefficient of rate-distortion optimization (RDO) mechanism is adaptively computed according to rate-QP and quality-QP models. The experimental results show that the proposed PVC solution has achieved two targets simultaneously: the quality of video sequence is kept consistently with an expected quality level and the bit rate saving of the proposed method is higher than traditional video coding standards and the relevant benchmark, notably with around 10% bitrate saving on average.
Related Topics
Concepts
Computer science
Video quality
Rate–distortion optimization
Multiview Video Coding
Quantization (signal processing)
Convolutional neural network
Artificial intelligence
Coding (social sciences)
Video tracking
Data compression
Video compression picture types
Real-time computing
Computer vision
Coding tree unit
Algorithm
Video processing
Metric (unit)
Decoding methods
Mathematics
Statistics
Operations management
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics12244905
- https://www.mdpi.com/2079-9292/12/24/4905/pdf?version=1701848386
- OA Status
- gold
- Cited By
- 4
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389390133
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389390133Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/electronics12244905Digital Object Identifier
- Title
-
Learning Adaptive Quantization Parameter for Consistent Quality Oriented Video CodingWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-06Full publication date if available
- Authors
-
Tien Vu Huu, Minh Ngoc, Sang Quang Nguyen, Huy Phi Cong, Thipphaphone Sisouvong, Xiem HoangVanList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics12244905Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/12/24/4905/pdf?version=1701848386Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2079-9292/12/24/4905/pdf?version=1701848386Direct OA link when available
- Concepts
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Computer science, Video quality, Rate–distortion optimization, Multiview Video Coding, Quantization (signal processing), Convolutional neural network, Artificial intelligence, Coding (social sciences), Video tracking, Data compression, Video compression picture types, Real-time computing, Computer vision, Coding tree unit, Algorithm, Video processing, Metric (unit), Decoding methods, Mathematics, Statistics, Operations management, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 3Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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