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.

翻译：基于深度学习的图像压缩技术近年来发展迅猛。为构建一种适用于图像压缩并可扩展至视频压缩的方法，我们提出了一种新颖的深度学习模型架构，将图像压缩任务分解为两个子任务：从亮度通道学习结构信息，从色度通道学习色彩信息。该模型设有两个独立分支，分别处理亮度分量与色度分量。在损失函数中采用色差度量CIEDE2000以优化模型的色彩保真度。我们展示了该方法的优势，并与其他编码器进行了性能对比。此外，还对潜在通道的脉冲响应进行了可视化与特征分析。