Content-Adaptive Resolution Control To Improve Video Coding Efficiency
| Autor: | Ihab Amer, Mehdi Saeedi, Ivanovic Boris, Shahram Shirani, Gabor Sines, Maryam Jenab, Liu Yang |
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| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
business.industry Computer science Quantization (signal processing) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications 02 engineering and technology Content adaptive Display resolution Algorithmic efficiency 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | ICME Workshops |
| Popis: | Aiming at improved rate-distortion (R-D) performance, this paper presents a machine-learning based solution for the run-time video resolution adaptation problem. The proposed approachutilizes neural networks that leverage a complexity feature extracted from the video frames topredict a quantization parameter (QP) for downscaled video targeting the same bitrate as the native video. The peak signal to noise ratio (PSNR) is also predicted for both the native and downscaled resolutions, and the one that leads to the highest PSNR is selected. Experimental results show that \quad the proposed adaptive approach achieves significant improvements in R-D performance compared to using a fixed resolution. |
| Databáze: | OpenAIRE |
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