Name
Abstract | ||
|---|---|---|
Rate Control (RC) plays an important role in video encoding. Traditional solutions are using fixed rate or fixed quantization parameters as the unified rate-control targets for all videos in one given video application. However, unified rate-control targets tend to have some bad encoding cases because of applying wrong rate for the video content. In this paper, we propose one content-adaptive rate control solution. We employ one neural-network based model which can end-to-end learn the optimal rate-control target appropriate to the content characteristics. The experimental results show that the proposed model can predict the optimal rate-factor value with the accuracy up to 77.637%. With this model, the proposed video-encoding method can significantly decrease the encoding quality fluctuation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/PCS48520.2019.8954541 | 2019 Picture Coding Symposium (PCS) |
Keywords | Field | DocType |
video content,content-adaptive rate control solution,neural-network based model,optimal rate-control target,optimal rate-factor value,video-encoding method,rate control target,content adaptive model,video encoding,fixed rate,fixed quantization parameters,unified rate-control targets,video application | Computer vision,Content adaptive,Video encoding,Computer science,Algorithm,Fixed interest rate loan,Artificial intelligence,Quantization (signal processing),Encoding (memory) | Conference |
ISSN | ISBN | Citations |
2330-7935 | 978-1-7281-4705-5 | 0 |
PageRank | References | Authors |
0.34 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huaifei Xing | 1 | 0 | 0.34 |
| Zhichao Zhou | 2 | 3 | 1.40 |
| Jialiang Wang | 3 | 0 | 0.34 |
| Huifeng Shen | 4 | 0 | 0.34 |
| He, D. | 5 | 33 | 13.67 |
| Fu Li | 6 | 0 | 1.35 |