针对全景拼接画质评估，首次实现从通用到个体的精准预测。

Existing image quality assessment (IQA) methods typically predict mean opinion scores (MOSs) but struggle to capture variations in individual subject behaviors. This limitation is highly pronounced in immersive visual applications such as stitched 360-degree panoramas, where user opinions diverge drastically based on personal sensitivity to blending-induced luminance inconsistency, detail loss, and geometric misalignment. Here we propose GC360IQ, a novel Generic-to-Individualized IQA framework that establishes a learned explicit feature space to characterize individual subject behaviors. First, we construct a specialized 360-degree panorama database focusing on blending-induced luminance and detail degradation while minimizing geometric misalignment, providing multidimensional quality ratings alongside complete individual scores. Second, we develop a generic quality model that utilizes unblended views as a perceptual reference. Dual feature extraction branches capture gradient and structural information specifically around stitching regions to predict baseline quality. Third, we construct a compact preference embedding space that acts as an explicit feature domain to model each subject's deviation from generic quality perceptions. We also introduce a maximum a posteriori (MAP) adaptation mechanism. By leveraging a preference prior learned within our explicit feature space, a new subject's unique behavioral embedding is mapped progressively with an increasing number of anchor ratings. Experiments demonstrate that the generic model provides accurate stitching quality predictions and that subject adaptation further improves individual score predictions. Deeper analysis of the collected ratings and learned embeddings reveals that observer differences contain structured variation related to scoring tendencies and sensitivity to stitching artifacts, rather than merely random rating noise.