Color Learning for Image Compression
Prativadibhayankaram S, Richter T, Sparenberg H, Foessel S (2023)
Publication Type: Conference contribution
Publication year: 2023
Journal
Publisher: IEEE Computer Society
Pages Range: 2330-2334
Conference Proceedings Title: Proceedings - International Conference on Image Processing, ICIP
Event location: Kuala Lumpur
ISBN: 9781728198354
DOI: 10.1109/ICIP49359.2023.10222731
Abstract
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.
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Germany (DE)How to cite
APA:
Prativadibhayankaram, S., Richter, T., Sparenberg, H., & Foessel, S. (2023). Color Learning for Image Compression. In Proceedings - International Conference on Image Processing, ICIP (pp. 2330-2334). Kuala Lumpur, MY: IEEE Computer Society.
MLA:
Prativadibhayankaram, Srivatsa, et al. "Color Learning for Image Compression." Proceedings of the 30th IEEE International Conference on Image Processing, ICIP 2023, Kuala Lumpur IEEE Computer Society, 2023. 2330-2334.
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