提出新指标评估生成图像的场景结构一致性，更精准反映构图变化。
A Novel Image Similarity Metric for Scene Composition Structure
- 基于立方体分层分割统计特征，无训练、纯分析方法衡量场景结构相似性。
- 对非构图扰动保持高度不变性，对构图改变呈现强单调下降趋势。
- 适合评估生成模型输出的结构准确性，尤其关注场景布局稳定性。
生成式AI模型的快速发展亟需超越人类感知的图像质量评估方法。一个关键挑战是保持图像底层的场景构图结构（SCS），即物体与背景之间的几何关系、相对位置、尺寸、朝向等。传统图像相似性度量在评估SCS方面表现不足：像素级方法对微小视觉噪声过于敏感，感知类指标侧重人类审美偏好，均无法有效捕捉结构保真度；而近期基于神经网络的度量则存在训练开销大和泛化能力弱的问题。本文提出一种全新的、无需训练的分析型度量——场景构图结构相似性指数（SCSSIM），通过图像的立方体分层划分所提取的统计特征，鲁棒地刻画非对象相关的结构关系。实验表明，SCSSIM对非构图性扰动具有高度不变性，能准确反映SCS未发生改变；而对构图性扰动则表现出强烈的单调下降，精确指示出结构是否被破坏。相较于现有度量，SCSSIM在结构评估上展现出更优特性，是开发与评估生成模型、保障场景构图完整性的有力工具。
原文摘要 · Abstract (English)
The rapid advancement of generative AI models necessitates novel methods for evaluating image quality that extend beyond human perception. A critical concern for these models is the preservation of an image's underlying Scene Composition Structure (SCS), which defines the geometric relationships among objects and the background, their relative positions, sizes, orientations, etc. Maintaining SCS integrity is paramount for ensuring faithful and structurally accurate GenAI outputs. Traditional image similarity metrics often fall short in assessing SCS. Pixel-level approaches are overly sensitive to minor visual noise, while perception-based metrics prioritize human aesthetic appeal, neither adequately capturing structural fidelity. Furthermore, recent neural-network-based metrics introduce training overheads and potential generalization issues. We introduce the SCS Similarity Index Measure (SCSSIM), a novel, analytical, and training-free metric that quantifies SCS preservation by exploiting statistical measures derived from the Cuboidal hierarchical partitioning of images, robustly capturing non-object-based structural relationships. Our experiments demonstrate SCSSIM's high invariance to non-compositional distortions, accurately reflecting unchanged SCS. Conversely, it shows a strong monotonic decrease for compositional distortions, precisely indicating when SCS has been altered. Compared to existing metrics, SCSSIM exhibits superior properties for structural evaluation, making it an invaluable tool for developing and evaluating generative models, ensuring the integrity of scene composition.
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