Multi-Objective Particle Swarm Optimization for ROI based Video Coding
| Autor: | Daiqin Yang, Xin Liu, Feiyang Liu, Yunfei Zhang, Yiyong Zha, Guangjie Ren |
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| Rok vydání: | 2019 |
| Předmět: |
Fitness function
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Particle swarm optimization 020207 software engineering 02 engineering and technology Multi-objective optimization Region of interest Distortion Bit rate 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm Coding (social sciences) |
| Zdroj: | MMAsia |
| DOI: | 10.1145/3338533.3366608 |
| Popis: | In this paper, we propose a new algorithm for High Efficiency Video Coding(HEVC) based on multi-objective particle swarm optimization (MOPSO) to enhance the visual quality of ROI while ensuring a certain overall quality. According to the R-λ model of detected ROI, the fitness function in MOPSO can be designed as the distortion of ROI and that of the overall frame. The particle consists of ROI's rate and other region's rate. After iterating through the multi-objective particle swarm optimization algorithm, the Pareto front is obtained. Then, the final bit allocation result which are the appropriate bit rate for ROI and non-ROI is selected from this set. Finally, according to the R-λ model, the coding parameters could be determined for coding. The experimental results show that the proposed algorithm improves the visual quality of ROI while guarantees overall visual quality. |
| Databáze: | OpenAIRE |
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