  In this paper, we tackle the problem of colorization of grayscale videos to
reduce bandwidth usage. For this task, we use some colored keyframes as
reference images from the colored version of the grayscale video. We propose a
model that extracts keyframes from a colored video and trains a Convolutional
Neural Network from scratch on these colored frames. Through the extracted
keyframes we get a good knowledge of the colors that have been used in the
video which helps us in colorizing the grayscale version of the video
efficiently. An application of the technique that we propose in this paper, is
in saving bandwidth while sending raw colored videos that haven't gone through
any compression. A raw colored video takes up around three times more memory
size than its grayscale version. We can exploit this fact and send a grayscale
video along with out trained model instead of a colored video. Later on, in
this paper we show how this technique can help to save bandwidth usage to upto
three times while transmitting raw colored videos.
