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A digital video is a collection of individual frames, while streaming thevideo the scene utilized the time slice for each frame. High refresh rate andhigh frame rate is the demand of all high technology applications. The actiontracking in videos becomes easier and motion becomes smoother in gamingapplications due to the high refresh rate. It provides a faster responsebecause of less time in between each frame that is displayed on the screen.FREGAN (Frame Rate Enhancement Generative Adversarial Network) model has beenproposed, which predicts future frames of a video sequence based on a sequenceof past frames. In this paper, we investigated the GAN model and proposedFREGAN for the enhancement of frame rate in videos. We have utilized Huber lossas a loss function in the proposed FREGAN. It provided excellent results insuper-resolution and we have tried to reciprocate that performance in theapplication of frame rate enhancement. We have validated the effectiveness ofthe proposed model on the standard datasets (UCF101 and RFree500). Theexperimental outcomes illustrate that the proposed model has a Peaksignal-to-noise ratio (PSNR) of 34.94 and a Structural Similarity Index (SSIM)of 0.95.

FREGAN : an application of generative adversarial networks in enhancing the frame rate of videos