Color Image Processing: Progress and Challenges

Dear Colleagues,

Color image processing has seen significant advances in recent decades, driven by improvements in imaging hardware, computational power, and machine learning. From early device-dependent pipelines to modern data-driven approaches, the field has progressed towards perceptually meaningful color representations. Today, the entire pipeline, from raw sensor capture to final display, includes white balance, color transformations, and enhancement, among others, all of which must function consistently across devices with varying gamuts and dynamic ranges.

A crucial requirement is maintaining a close connection to raw sensor data, avoiding early irreversible processes. This allows for more accurate color mapping and consistent displays, helping to sustain a stable visual experience. Nonetheless, balancing physical accuracy with perceptual quality remains challenging, especially as imaging systems and viewing conditions grow more diverse.

A key issue is color constancy, the capability to perceive stable object colors under changing illumination. Although humans do this effortlessly, computational models still face difficulties in complex environments. Classical methods depend on simplifying assumptions, while learning-based approaches enhance performance but often lack interpretability and robustness.

Insights from the human visual system, particularly adaptation mechanisms, are vital for developing effective algorithms for white balance and color constancy. These challenges are heightened in extended reality (XR), where real and virtual content must be seamlessly combined. Future advancements depend on integrating physics-based models with perceptual insights to deliver consistent and immersive color experiences.

Dr. Raquel Gil Rodríguez
Guest Editor

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