On the Game Day Sideline: A Case Study of Web-based AI Color Correction for Social Media Content

Both sports brands and their fans value brand color accuracy across all mediums from jumbotron footage at a live event to social media content. ColorNet, a trio of color correction tools developed at Clemson University, addressed this concern. ColorNet 1.0 used a patented algorithm trained with paired datasets, ColorNet 2.0 addressed some weaknesses in ColorNet 1.0 and achieved similar results using machine learning segmentation, and ColorNet 3.0 was a combined hardware and software solution that allowed content creators to identify distortions in the color spectrum and apply appropriate targeted adjustments. This case study implemented ColorNet 1.5 as a web application intended for sideline, game day use by content creators to ensure brand color accuracy for social media content. Results show that content creators who participated in this study highly value and can identify brand color correctness and that increased processing speed would be a necessity for production-level implementation of this tool.