FREGAN : an application of generative adversarial networks in enhancing the frame rate of videos
This addresses the need for smoother motion and faster response in high-tech applications like gaming, though it appears incremental as it applies existing GAN techniques to a specific video enhancement task.
The paper tackles the problem of enhancing video frame rates by predicting future frames from past sequences using a GAN-based model called FREGAN, achieving a PSNR of 34.94 and SSIM of 0.95 on standard datasets.
A digital video is a collection of individual frames, while streaming the video the scene utilized the time slice for each frame. High refresh rate and high frame rate is the demand of all high technology applications. The action tracking in videos becomes easier and motion becomes smoother in gaming applications due to the high refresh rate. It provides a faster response because of less time in between each frame that is displayed on the screen. FREGAN (Frame Rate Enhancement Generative Adversarial Network) model has been proposed, which predicts future frames of a video sequence based on a sequence of past frames. In this paper, we investigated the GAN model and proposed FREGAN for the enhancement of frame rate in videos. We have utilized Huber loss as a loss function in the proposed FREGAN. It provided excellent results in super-resolution and we have tried to reciprocate that performance in the application of frame rate enhancement. We have validated the effectiveness of the proposed model on the standard datasets (UCF101 and RFree500). The experimental outcomes illustrate that the proposed model has a Peak signal-to-noise ratio (PSNR) of 34.94 and a Structural Similarity Index (SSIM) of 0.95.