Color image stitching is a challenging task in real-world applications. This paper first proposes a quaternion rank-1 alignment (QR1A) model for high-precision color image alignment. To solve the optimization problem of QR1A, we develop a nested iterative algorithm under the framework of complex-valued alternating direction method of multipliers. To quantitatively evaluate image stitching performance, we propose a perceptual seam quality (PSQ) measure to calculate misalignments of local regions along the seamline. Using QR1A and PSQ, we further propose an automatic color image stitching (ACIS-QR1A) framework. In this frame-work, the automatic strategy and iterative learning strategy are developed to simultaneously learn the optimal seamline and local alignment. Extensive experiments on challenging datasets demonstrate that the proposed ACIS-QR1A is able to obtain high-quality stitched images under several difficult scenarios including large parallax, low textures, moving objects, large occlusions or/and their combinations.