论文标题
Movidnn：一个用于评估视频质量增强的移动平台，深层神经网络
MoViDNN: A Mobile Platform for Evaluating Video Quality Enhancement with Deep Neural Networks
论文作者
论文摘要
深入研究了基于深的神经网络（DNN）方法，以提高视频质量，这要归功于它们近年来的快速发展。这些方法主要是为台式设备而设计的，因为它们的计算成本很高。但是，随着近年来移动设备的性能不断提高，可以在移动设备中执行基于DNN的方法。尽管具有所需的计算能力，但利用DNN来提高移动设备的视频质量仍然是一个活跃的研究领域。在本文中，我们提出了一个开源移动平台，即Movidnn，以评估基于DNN的视频质量增强方法，例如超分辨率，脱氧和拆除。我们提出的平台可用于客观和主观地评估基于DNN的方法。为了进行客观的评估，我们报告了常见的指标，例如执行时间，PSNR和SSIM。对于主观评估，报告平均得分意见（MOS）。该建议的平台可在https://github.com/cd-athena/movidnn公开获得
Deep neural network (DNN) based approaches have been intensively studied to improve video quality thanks to their fast advancement in recent years. These approaches are designed mainly for desktop devices due to their high computational cost. However, with the increasing performance of mobile devices in recent years, it became possible to execute DNN based approaches in mobile devices. Despite having the required computational power, utilizing DNNs to improve the video quality for mobile devices is still an active research area. In this paper, we propose an open-source mobile platform, namely MoViDNN, to evaluate DNN based video quality enhancement methods, such as super-resolution, denoising, and deblocking. Our proposed platform can be used to evaluate the DNN based approaches both objectively and subjectively. For objective evaluation, we report common metrics such as execution time, PSNR, and SSIM. For subjective evaluation, Mean Score Opinion (MOS) is reported. The proposed platform is available publicly at https://github.com/cd-athena/MoViDNN
