A video processing system includes a computing platform having a hardware processor and a memory storing a software code including a convolutional neural network (CNN). The hardware processor executes the software code to receive video data including a key video frame in color and a video sequence in gray scale, determine a first estimated colorization for each frame of the video sequence except the key video frame based on a colorization of a previous frame, and determine a second estimated colorization for each frame of the video sequence except the key video frame based on the key video frame in color. For each frame of the video sequence except the key video frame, the software code further blends the first estimated colorization with the second estimated colorization using a color fusion stage of the CNN to produce a colorized video sequence corresponding to the video sequence in gray scale.
BACKGROUND(1) Color propagation has a wide range of applications in video processing. For example, color propagation may be utilized as part of the work flow in filmmaking, where color modification for artistic purposes typically plays an important role. In addition, color propagation may be used in the restoration and colorization of heritage film footage.(2) Conventional approaches for color propagation often rely on optical flow computation to propagate colors in a video sequence from fully colored video frames. However, estimating the correspondence maps utilized in optical flow approaches to color propagation is computationally expensive and tends to be error prone. Unfortunately, inaccuracies in optical flow can lead to color artifacts which accumulate over time. Consequently, there remains a need in the art for a more efficient video color propagation.SUMMARY(3) There are provided systems and methods for performing video color propagation, substantially as shown in and/or described in connection with at least one of the figures, and as set forth more completely in the claims.
1. A video processing system comprising: a computing platform including a hardware processor and a system memory storing a software code including a convolutional neural network (CNN); the hardware processor configured to execute the software code to: receive a video data including a key video frame in color and a video sequence in gray scale beginning with the key video frame; determine a first estimated colorization for each frame of the video sequence except the key video frame based on a colorization of a previous neighboring frame of the video sequence; determine a second estimated colorization for each frame of the video sequence except the key video frame based on the key video frame in color; and for each frame of the video sequence except the key video frame, blend the first estimated colorization for the each frame with the second estimated colorization for the each frame using a color fusion stage of the CNN to produce a colorized video sequence corresponding to the video sequence in gray scale.
9. A method for use by a video processing system including a computing platform having a hardware processor and a system memory storing a software code including a convolutional neural network (CNN), the method comprising: receiving, using the hardware processor, a video data including a key video frame in color and a video sequence in gray scale beginning with the key video frame; determining, using the hardware processor, a first estimated colorization for each frame of the video sequence except the key video frame based on a colorization of a previous neighboring frame of the video sequence; determining, using the hardware processor, a second estimated colorization for each frame of the video sequence except the key video frame based on the key video frame in color; and for each frame of the video sequence except the key video frame, blend the first estimated colorization for the each frame with the second estimated colorization for the each frame, using the hardware processor and a color fusion stage of the CNN, to produce a colorized video sequence corresponding to the video sequence in gray scale.
17. A video compression method for use by a computing platform having a hardware processor, the method comprising: receiving, using the hardware processor, a video data including a key video frame in color and a video sequence in gray scale beginning with the key video frame; determining, using the hardware processor, a first estimated colorization for each frame of the video sequence except the key video frame based on a colorization of a previous neighboring frame of the video sequence; determining, using the hardware processor, a second estimated colorization for each frame of the video sequence except the key video frame based on the key video frame in color; and for each frame of the video sequence except the key video frame, blend the first estimated colorization for the each frame with the second estimated colorization for the each frame, using the hardware processor and a color fusion stage of a convolutional neural network (CNN), to produce a colorized video sequence corresponding to the video sequence in gray scale.