Colorless Video Rendering System Via Generative Adversarial Networks

This paper is an extension of work originally presented in 2019 IEEE International Conference on Artificial Intelligence and Computer Applications(ICAICA).With the development of computer science and technology,movies have developed from the simple black-and-white film era to the current digital age.Coloring old movies is a difficult task,aside from the traditional hand-painting techniques,the most common method is to use post-processing software for coloring movie frames.This kind of operation requires extraordinary skills,patience and aesthetics,which is a great test for the operator.In recent years,the extensive use of machine learning and neural networks has made it possible for computers to intelligently process images.Since 2016,various types of generative adversarial networks models have been proposed to make deep learning shine in the fields of image style transfer,image coloring,and image style change.In this case,the experiment uses the generative adversarial networks principle to process pictures and videos to realize the automatic rendering of old documentary movies.Combined with analysis,we carry out a deep learning training for black and white documentaries' coloring,furthermore,we provide an improved generative adversarial networks.Based on the destruction and construction of picture information,the improved algorithm added a self-attention mechanism to achieve an end-to-end training.In the reasoning process,it provides precise rendering predictions and larger information areas.The result indicated that the color configuration conforms to people's normal aesthetic standards,besides,the synthesized video flame provides a more sufficient information and has better viewing quality.