图像风格化中音乐感知的审美要素：一种情感驱动的音乐到视觉操控方法
Aesthetic Matters in Music Perception for Image Stylization: A Emotion-driven Music-to-Visual Manipulation
AI总结：
本文提出情感驱动的音乐到视觉操控方法EmoMV，结合音乐元素自下而上处理与视觉情感自上而下应用，并通过图像质量、审美和EEG多尺度评估验证其能将音乐情感有效转化为视觉图像，推动多模态情感融合。
摘要：
情感信息对于增强人机交互和深化图像理解至关重要。然而，尽管深度学习推动了图像识别的发展，对图像中情感表达的直观理解和精确控制仍然具有挑战性。同样，音乐研究主要聚焦于理论层面，对其情感维度及其与视觉艺术融合的探索较为有限。为填补这些空白，我们提出了EmoMV，这是一种情感驱动的音乐到视觉操控方法，能够基于音乐情感对图像进行操控。EmoMV将自下而上的音乐元素处理——如音高和节奏——与自上而下地将这些情感应用于色彩和光照等视觉方面的过程相结合。我们采用多尺度框架对EmoMV进行评估，该框架包括图像质量指标、审美评估以及用于捕捉实时情感反应的脑电图（EEG）测量。结果表明，EmoMV能够有效地将音乐的情感内容转化为具有视觉吸引力的图像，推动了多模态情感融合的发展，并为创意产业和交互技术开辟了新的途径。
英文摘要：
Emotional information is essential for enhancing human-computer interaction and deepening image understanding. However, while deep learning has advanced image recognition, the intuitive understanding and precise control of emotional expression in images remain challenging. Similarly, music research largely focuses on theoretical aspects, with limited exploration of its emotional dimensions and their integration with visual arts. To address these gaps, we introduce EmoMV, an emotion-driven music-to-visual manipulation method that manipulates images based on musical emotions. EmoMV combines bottom-up processing of music elements-such as pitch and rhythm-with top-down application of these emotions to visual aspects like color and lighting. We evaluate EmoMV using a multi-scale framework that includes image quality metrics, aesthetic assessments, and EEG measurements to capture real-time emotional responses. Our results demonstrate that EmoMV effectively translates music's emotional content into visually compelling images, advancing multimodal emotional integration and opening new avenues for creative industries and interactive technologies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
