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

TL;DR

FREGAN employs generative adversarial networks with Huber loss to predict and enhance video frame rates, resulting in smoother motion and improved video quality for applications like gaming and action tracking.

Contribution

This paper introduces FREGAN, a novel GAN-based model utilizing Huber loss for effective video frame rate enhancement, validated on standard datasets.

Findings

Achieved PSNR of 34.94 indicating high-quality frame prediction.

Attained SSIM of 0.95 demonstrating structural similarity.

Validated effectiveness on UCF101 and RFree500 datasets.

Abstract

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.