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In this paper, we tackle the problem of colorization of grayscale videos toreduce bandwidth usage. For this task, we use some colored keyframes asreference images from the colored version of the grayscale video. We propose amodel that extracts keyframes from a colored video and trains a ConvolutionalNeural Network from scratch on these colored frames. Through the extractedkeyframes we get a good knowledge of the colors that have been used in thevideo which helps us in colorizing the grayscale version of the videoefficiently. An application of the technique that we propose in this paper, isin saving bandwidth while sending raw colored videos that haven't gone throughany compression. A raw colored video takes up around three times more memorysize than its grayscale version. We can exploit this fact and send a grayscalevideo along with out trained model instead of a colored video. Later on, inthis paper we show how this technique can help to save bandwidth usage to uptothree times while transmitting raw colored videos.

Video Colorization using CNNs and Keyframes extraction: An application in saving bandwidth