Color Learning for Image Compression
Srivatsa Prativadibhayankaram Thomas Richter Heiko Sparenberg Siegfried Fößel
Deep learning based image compression has gained a lot of momentum in recent
times. To enable a method that is suitable for image compression and
subsequently extended to video compression, we propose a novel deep learning
model architecture, where the task of image compression is divided into two
sub-tasks, learning structural information from luminance channel and color
from chrominance channels. The model has two separate branches to process the
luminance and chrominance components. The color difference metric CIEDE2000 is
employed in the loss function to optimize the model for color fidelity. We
demonstrate the benefits of our approach and compare the performance to other
codecs. Additionally, the visualization and analysis of latent channel impulse
response is performed.