Constructing a No-Reference H.264/AVC Bitstream-Based Video Quality Metric Using Genetic Programming-Based Symbolic Regression
| Autor: | Brecht Vermeulen, Dirk Deschrijver, Ekaterina Vladislavleva, Nicolas Staelens, Piet Demeester, Tom Dhaene |
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| Rok vydání: | 2013 |
| Předmět: |
VISIBILITY
objective video quality metric Technology and Engineering Computer science Genetic programming Video quality Machine learning computer.software_genre Media Technology Computer vision Quality of experience Electrical and Electronic Engineering Bitstream no-reference Subjective video quality business.industry high definition MPEG-2 VIDEO Human visual system model Metric (mathematics) IBCN H.264/AVC NEURAL-NETWORKS Artificial intelligence Symbolic regression business quality of experience (QoE) computer |
| Zdroj: | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY |
| ISSN: | 1558-2205 1051-8215 |
| Popis: | In order to ensure optimal quality of experience toward end users during video streaming, automatic video quality assessment becomes an important field-of-interest to video service providers. Objective video quality metrics try to estimate perceived quality with high accuracy and in an automated manner. In traditional approaches, these metrics model the complex properties of the human visual system. More recently, however, it has been shown that machine learning approaches can also yield competitive results. In this paper, we present a novel no-reference bitstream-based objective video quality metric that is constructed by genetic programming-based symbolic regression. A key benefit of this approach is that it calculates reliable white-box models that allow us to determine the importance of the parameters. Additionally, these models can provide human insight into the underlying principles of subjective video quality assessment. Numerical results show that perceived quality can be modeled with high accuracy using only parameters extracted from the received video bitstream. |
| Databáze: | OpenAIRE |
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