EVSRNet: Efficient Video Super-Resolution with Neural Architecture Search
Shaoli Liu, Chengjian Zheng, Kaidi Lu, Si Gao, Ning Wang, Bofei Wang, Diankai Zhang, Xiaofeng Zhang, Tianyu Xu
Abstract
With the development of convolutional neural networks (CNN), the super-resolution results of CNN-based method have far surpassed traditional method. In particular, the CNN-based single image super-resolution method has achieved excellent results. Video sequences contain more abundant information compare with image, but there are few video super-resolution methods that can be applied to mobile devices due to the requirement of heavy computation, which limits the application of video super-resolution. In this work, we propose the Efficient Video Super-Resolution Network (EVSRNet) with neural architecture search for real-time video super-resolution. Extensive experiments show that our method achieves a good balance between quality and efficiency. Finally, we achieve a competitive result of 7.36 where the PSNR is 27.85 dB and the inference time is 11.3 ms/f on the target snapdragon 865 SoC, resulting in a 2nd place in the Mobile AI (MAI) 2021 real-time video super-resolution challenge. It is noteworthy that, our method is the fastest and significantly outperforms other competitors by large margins.