Aesthetic Matters in Music Perception for Image Stylization: A Emotion-driven Music-to-Visual Manipulation

TL;DR

This paper presents EmoMV, a novel method that uses musical emotions to guide image stylization, enhancing emotional expression and aesthetic appeal through multimodal integration.

Contribution

Introduces EmoMV, a new approach that combines music emotional analysis with visual manipulation for improved emotional and aesthetic image rendering.

Findings

EmoMV effectively translates musical emotions into visual styles.

The method improves aesthetic quality and emotional expressiveness of images.

EEG measurements confirm real-time emotional engagement.

Abstract

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.