Color Learning for Image Compression

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 s...

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Bibliographic Details
Published in:arXiv.org 2023-06
Main Authors: Srivatsa Prativadibhayankaram, Richter, Thomas, Sparenberg, Heiko, Fößel, Siegfried
Format: Article
Language:English
Subjects:
Codec
Color
Deep learning
Image compression
Impulse response
Luminance
Video compression
Online Access:Get full text
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Description
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2306.17460