Learning Camera-Agnostic White-Balance Preferences

• URL: http://arxiv.org/abs/2507.01342v1
• Date: Wed, 02 Jul 2025 04:11:01 GMT
• Title: Learning Camera-Agnostic White-Balance Preferences
• Authors: Luxi Zhao, Mahmoud Afifi, Michael S. Brown,
• Abstract summary: Post-illuminant-estimation mapping transforms neutral illuminant corrections into preferred corrections in a camera-agnostic space.<n>Our proposed model is lightweight -- containing only $sim$500 parameters -- and runs in just 0.024 milliseconds on a typical flagship mobile CPU.
• Score: 30.282495562510285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The image signal processor (ISP) pipeline in modern cameras consists of several modules that transform raw sensor data into visually pleasing images in a display color space. Among these, the auto white balance (AWB) module is essential for compensating for scene illumination. However, commercial AWB systems often strive to compute aesthetic white-balance preferences rather than accurate neutral color correction. While learning-based methods have improved AWB accuracy, they typically struggle to generalize across different camera sensors -- an issue for smartphones with multiple cameras. Recent work has explored cross-camera AWB, but most methods remain focused on achieving neutral white balance. In contrast, this paper is the first to address aesthetic consistency by learning a post-illuminant-estimation mapping that transforms neutral illuminant corrections into aesthetically preferred corrections in a camera-agnostic space. Once trained, our mapping can be applied after any neutral AWB module to enable consistent and stylized color rendering across unseen cameras. Our proposed model is lightweight -- containing only $\sim$500 parameters -- and runs in just 0.024 milliseconds on a typical flagship mobile CPU. Evaluated on a dataset of 771 smartphone images from three different cameras, our method achieves state-of-the-art performance while remaining fully compatible with existing cross-camera AWB techniques, introducing minimal computational and memory overhead.

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