Learning Camera-Agnostic White-Balance Preferences

TL;DR

This paper introduces a lightweight, camera-agnostic method for transforming neutral white-balance corrections into aesthetically preferred ones, enabling consistent stylized color rendering across different smartphone cameras.

Contribution

It presents the first approach to learn a post-illuminant-estimation mapping for aesthetic white balance that generalizes across unseen cameras, with minimal computational overhead.

Findings

Achieves state-of-the-art aesthetic white balance performance.

Runs in 0.024 ms on a mobile CPU.

Compatible with existing cross-camera AWB methods.

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.