Learning-Based Perceptual Image Quality Improvement for Video Conferencing

It is well known that in professional TV show filming, stage lighting has to be carefully designed in order to make the host and the scene look visually appealing. The lighting affects not only the brightness but also the color tone which plays a critical role in the perceived look of the host and the mood of the stage. In contrast, during video conferencing, the lighting is usually far from ideal thus the perceived image quality is low. There has been a lot of research on improving the brightness of the captured images, but as far as we know, there has not been any work addressing the color tone issue. In this paper, we propose a learning-based technique to improve the perceptual image quality during video conferencing. The basic idea is to learn the color statistics from a training set of images which look visually appealing, and adjust the color of an input image so that its color statistics matches those in the training set. To validate our approach, we have conducted user study and the results show that our technique significantly improves the perceived image quality.