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Deep neural network (DNN) based approaches have been intensively studied toimprove video quality thanks to their fast advancement in recent years. Theseapproaches are designed mainly for desktop devices due to their highcomputational cost. However, with the increasing performance of mobile devicesin recent years, it became possible to execute DNN based approaches in mobiledevices. Despite having the required computational power, utilizing DNNs toimprove the video quality for mobile devices is still an active research area.In this paper, we propose an open-source mobile platform, namely MoViDNN, toevaluate DNN based video quality enhancement methods, such as super-resolution,denoising, and deblocking. Our proposed platform can be used to evaluate theDNN based approaches both objectively and subjectively. For objectiveevaluation, we report common metrics such as execution time, PSNR, and SSIM.For subjective evaluation, Mean Score Opinion (MOS) is reported. The proposedplatform is available publicly at https://github.com/cd-athena/MoViDNN

MoViDNN: A Mobile Platform for Evaluating Video Quality Enhancement with Deep Neural Networks