GC360IQ：面向拼接360度全景图的通用到个性化质量评估
GC360IQ: Generic-to-Individualized Quality Assessment for Stitched 360-Degree Panoramas
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中文总结 AI 辅助
提出GC360IQ框架，通过构建专用数据库、通用质量模型和偏好嵌入空间及MAP自适应，实现拼接360度全景图的通用到个性化质量评估，提升个体评分预测准确性。
中文摘要 AI 辅助
现有的图像质量评估（IQA）方法通常预测平均意见分数（MOS），但难以捕捉个体受试者行为的差异。这一局限性在沉浸式视觉应用（如拼接360度全景图）中尤为突出，因为用户意见会因个人对拼接引起的亮度不一致、细节丢失和几何错位的敏感度不同而大幅分化。在此，我们提出GC360IQ，一种新颖的通用到个性化IQA框架，该框架建立了一个学习到的显式特征空间来表征个体受试者行为。首先，我们构建了一个专门的360度全景图数据库，聚焦于拼接引起的亮度和细节退化，同时最小化几何错位，提供多维质量评分以及完整的个体评分。其次，我们开发了一个通用质量模型，利用未拼接视图作为感知参考。双特征提取分支捕获拼接区域周围的梯度和结构信息，以预测基线质量。第三，我们构建了一个紧凑的偏好嵌入空间，作为显式特征域来建模每个受试者相对于通用质量感知的偏差。我们还引入了一种最大后验（MAP）自适应机制。通过利用在我们的显式特征空间中学习到的偏好先验，新受试者的独特行为嵌入随着锚定评分数量的增加而逐步映射。实验表明，通用模型提供了准确的拼接质量预测，且受试者自适应进一步改善了个体评分预测。对收集的评分和学习到的嵌入的深入分析揭示，观察者差异包含与评分倾向和对拼接伪影敏感性相关的结构化变化，而不仅仅是随机评分噪声。
英文摘要
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.
发表机构
- University of Waterloo（滑铁卢大学）
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