mlsanityData to 14 Oct 2026

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

arXiv 2111.01105 ↗First posted 1 Nov 2021cs.CV cs.LG eess.IVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 26 references that are in the map. First posted on arXiv 1 Nov 2021.

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References · 16

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4 more
  1. 2020Blurry Video Frame Interpolationcited by 57
  2. 2018FutureGAN: Anticipating the Future Frames of Video Sequences using Spatio-Temporal 3d Convolutions in Progressively Growing GANscited by 24
  3. 2016Geometry-Based Next Frame Prediction from Monocular Videocited by 6
  4. 2018Wasserstein GAN and Waveform Loss-based Acoustic Model Training for Multi-speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocodercited by 3

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