让相机实时生成符合情绪的影像，提升视觉表达力。
EMOVIS: Emotion-Optimized Image Processing
- 将情绪状态与色彩饱和度等参数建立映射关系
- 用户测试显示87%场景下情绪匹配时更受偏好
- 无需改动硬件即可集成到现有相机流程
在影视制作中，色彩分级、对比度和亮度等视觉属性被用来强化场景的情感叙事。然而，传统图像信号处理器（ISP）侧重于场景保真度，忽略了这一表现维度。为将这种电影级表现力引入视频拍摄的实时相机管线，我们提出 EMOVIS（EMotion-Optimized VISual processing）。通过一次具有统计显著性的校准用户研究，我们建立了高阶情绪状态（快乐、平静、愤怒、悲伤）与低阶ISP控制参数（包括色彩饱和度、局部调色映射和锐度）之间的系统性映射。我们设计了一个控制框架，将这些情绪驱动的调整无缝集成至标准ISP硬件中，而无需改变底层处理阶段。通过盲测A/B实验验证，当目标情绪与场景语境一致时，观众在87%的测试中更偏好经过情绪优化的渲染结果，表明情绪对齐的ISP控制能显著提升视觉内容的表现适配性。
原文摘要 · Abstract (English)
In cinematography, visual attributes such as color grading, contrast, and brightness are manipulated to reinforce the emotional narrative of a scene. However, conventional Image Signal Processors (ISPs) prioritize scene fidelity, effectively neglecting this expressive dimension. To bring this cinematic capability to real-time camera pipelines during video capture, we introduce EMOVIS (EMotion-Optimized VISual processing). We establish a systematic mapping between a compact set of high-level emotional states (Happy, Calm, Angry, Sad) and low-level ISP controls - including color saturation, local tone mapping, and sharpness - supported by a calibration user study with statistically significant effects across parameters. We propose a control framework that integrates these emotion-driven adjustments into standard ISP hardware without altering the underlying processing stages. Validation via blind A/B testing shows that viewers prefer the emotion-optimized rendering in 87% of trials when the target emotion matches the scene context, indicating that emotion-aligned ISP control improves perceived suitability for expressive visual content.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。