ColorNet 2.0: Image Segmentation for Brand Color Correction in Video

Image segmentation involves partitioning an image into multiple regions based on the characteristics of pixels in the image. ColorNet 2.0, a machine learning model, uses segmentation to color correct images without needing to be retrained to correct each individual color. Users choose a target color and have areas of the image that perceptually match the target color selected for adjustment. ColorNet 2.0 improves on the limitations of previous models by allowing multiple colors to be targeted and adjusted by a single model. Initial tests of this model are promising, but some improvements are still needed before being used in a production setting. Future work will focus on improving both the color adjustment code and the efficiency of the neural network architecture to apply adjustments in real time.