Distinguishing Intrinsic Scene Colors from Color Casts for sRGB White Balance Correction

White balance (WB) correction for sRGB images requires removing color casts introduced by inaccurate WB settings while preserving intrinsic scene colors. Although existing methods achieve competitive WB correction performance, nonlinear image signal (ISP) processing entangles intrinsic scene colors with color casts, making it difficult to determine which chromatic variations should be corrected and which should be preserved, especially when large regions of intrinsic scene color dominate the image and resemble color casts, which can lead to inaccurate correction. To alleviate this correction ambiguity, we introduce the color cast state (CCS), an image-level representation that explicitly characterizes the tendency and severity of color casts, and propose a CCS-guided framework for sRGB white balance correction. Specifically, we learn a structured CCS representation from paired incorrectly white-balanced and ground-truth images using chromatic tendency and severity supervision together with soft similarity learning, thereby preserving continuous relationships among different color cast states. The learned CCS is then used as an explicit condition for the correction network, enabling correction that better preserves intrinsic scene colors while removing color casts. Extensive experiments on public benchmark datasets demonstrate that our method achieves state-of-the-art performance and consistently performs favorably under different WB settings, delivering more visually pleasing correction results.