Low-Complexity Scaler Based on Convolutional Neural Networks for Adaptive Video Streaming

Adaptive streaming service nowadays, became an essential key technology for delivering videos over internet protocol network. Due to limitations and fluctuations in internet bandwidth, scaler has become essential for streaming service. Recently, learning-based scalers have greatly improved performance compared to conventional methods in visual quality. However, performance is not guaranteed for compatibility of conventional scaler and real-time processing is difficult due to high complexity. In this paper, we present a low complexity scaler based on convolutional neural networks called Video Scale Network (VSN). The proposed method has a simple structure with real-time processing and a loss function compatible of conventional scaler. Furthermore, we propose a learning method to improve performance using only a single network. Experimental results on the AOM test sequences reveal improved performance by using the proposed method compared to the conventional method and real-time processing in the decoder of AV1 codec is also able to be done.