# EMOVIS: Emotion-Optimized Image Processing

arXiv: https://arxiv.org/abs/2605.03131 · HTML: https://mlsanity.com/paper/2605.03131/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 4 May 2026.

## Builds on (most cited first, 6 of 6)

- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes](https://mlsanity.com/paper/2307.07961/) (2023)
- [AdaptiveISP: Learning an Adaptive Image Signal Processor for Object Detection](https://mlsanity.com/paper/2410.22939/) (2024)
- [VEATIC: Video-based Emotion and Affect Tracking in Context Dataset](https://mlsanity.com/paper/2309.06745/) (2023)
- [Automatic Image Transformation for Inducing Affect](https://mlsanity.com/paper/1707.08148/) (2017)
- [Cuid: A new study of perceived image quality and its subjective assessment](https://mlsanity.com/paper/2009.13304/) (2020)

## Abstract (arXiv)

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.