Deep chroma compression of tone-mapped images
Xenios Milidonis, Francesco Banterle, Alessandro Artusi

TL;DR
This paper introduces a GAN-based method for fast, accurate chroma compression of HDR tone-mapped images, improving color fidelity and enabling real-time processing on resource-limited devices.
Contribution
It proposes a novel generative adversarial network with a hue-aware loss function for efficient chroma compression in HDR images, outperforming existing methods.
Findings
Outperforms state-of-the-art in color accuracy
Achieves real-time processing on limited hardware
Maintains or improves visual quality of compressed images
Abstract
Acquisition of high dynamic range (HDR) images is thriving due to the increasing use of smart devices and the demand for high-quality output. Extensive research has focused on developing methods for reducing the luminance range in HDR images using conventional and deep learning-based tone mapping operators to enable accurate reproduction on conventional 8 and 10-bit digital displays. However, these methods often fail to account for pixels that may lie outside the target display's gamut, resulting in visible chromatic distortions or color clipping artifacts. Previous studies suggested that a gamut management step ensures that all pixels remain within the target gamut. However, such approaches are computationally expensive and cannot be deployed on devices with limited computational resources. We propose a generative adversarial network for fast and reliable chroma compression of HDR…
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Taxonomy
TopicsImage Enhancement Techniques · Advanced Data Compression Techniques · Video Coding and Compression Technologies
