EMOVIS: Emotion-Optimized Image Processing

TL;DR

This paper introduces EMOVIS, a system that adjusts camera image processing parameters to evoke specific emotions, enhancing cinematic expressiveness in real-time video capture.

Contribution

It establishes a systematic mapping between high-level emotions and low-level ISP controls, enabling emotion-driven image adjustments without altering hardware.

Findings

Viewers preferred emotion-optimized images in 87% of trials when matching scene context.

A calibration study confirmed significant effects of parameters on perceived emotion.

The control framework integrates seamlessly with standard ISP hardware.

Abstract

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.