Colorless Video Rendering System via Generative Adversarial Networks
Title | Colorless Video Rendering System via Generative Adversarial Networks |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Cui, Yongcheng, Wang, Wenyong |
Conference Name | 2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) |
Keywords | aesthetics, ART, automatic rendering, black- film era, coloring movie frames, coloring old movies, colorless video rendering system, computer images, convolutional neural networks, current digital age, Deep Learning, digital photography, Gallium nitride, Generative Adversarial Learning, generative adversarial networks, generative adversarial networks models, generative adversarial networks principle, Generators, human culture, Image color analysis, image coloring, image colour analysis, image re-coloring, image style, learning (artificial intelligence), machine learning, manual stage, Metrics, Motion pictures, neural nets, Neural networks, old documentary movies, patience, post-processing software, pubcrawl, rendering (computer graphics), resilience, Resiliency, Scalability, self-attention GAN, traditional hand-painting techniques, Training, white film era |
Abstract | In today's society, even though the technology is so developed, the coloring of computer images has remained at the manual stage. As a carrier of human culture and art, film has existed in our history for hundred years. With the development of science and technology, movies have developed from the simple black-and-white film era to the current digital age. There is a very complicated process for coloring old movies. 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. |
URL | https://ieeexplore.ieee.org/document/8873434/ |
DOI | 10.1109/ICAICA.2019.8873434 |
Citation Key | cui_colorless_2019 |
- patience
- image re-coloring
- image style
- learning (artificial intelligence)
- machine learning
- manual stage
- Metrics
- Motion pictures
- neural nets
- Neural networks
- old documentary movies
- image colour analysis
- post-processing software
- pubcrawl
- rendering (computer graphics)
- resilience
- Resiliency
- Scalability
- self-attention GAN
- traditional hand-painting techniques
- Training
- white film era
- digital photography
- ART
- automatic rendering
- black- film era
- coloring movie frames
- coloring old movies
- colorless video rendering system
- computer images
- convolutional neural networks
- current digital age
- deep learning
- aesthetics
- Gallium nitride
- Generative Adversarial Learning
- generative adversarial networks
- generative adversarial networks models
- generative adversarial networks principle
- Generators
- human culture
- Image color analysis
- image coloring