Video Prediction using Recurrent Neural Network

With the advent of recent technologies, Deep Learning, a subset of machine learning, has gained popularity in solving problems in a variety of domains. One vast field of application for Deep Learning approaches is in the generation of video frames. While Video Interpolation has come a long way, Deep Learning based Video Prediction remains a prominent area of research. We implement a recurrent neural network for the task of video prediction, and then analyze the performance of the model for several different generation ratios and examine the impact of variations in the videos on the model’s ability to stick close to the ground truth. The model is tested on raw videos from Youtube of the ‘Comedian’ category. The quality of the model is evaluated using quantitative and qualitative metrics. The potential use case can be in video streaming to reduce the transmission bandwidth and to generate frames that are not present in the video.