Visplid

Visplid is a concept used in visual computing to quantify perceived visual stability during motion. It describes how coherent a viewer's perception remains when a scene contains multiple moving elements, depth cues, and occlusions. In practice, visplid serves as a scalar metric derived from psychophysical experiments and algorithmic analysis of motion fields, intended to assist designers and engineers in evaluating animation quality, virtual reality comfort, and video encoding efficiency.

The term was introduced in 2018 by researchers at the Institute of Visual Perception, who proposed visplid as a lightweight proxy for subjective stability. The word is a blend of 'visual' and 'stability' with the suffix '-id' used in perceptual measures. The concept has since been cited in studies examining perceptual quality in dynamic imagery and related visualization pipelines.

Visplid values are obtained from controlled user studies in which participants rate stability of stimuli, combined with objective cues such as motion coherence, parallax consistency, and flicker sensitivity. Computational estimators aim to predict visplid from scene parameters like camera motion, object speed, and rendering quality, producing scores on a 0–1 scale. Researchers emphasize that visplid is meant as a practical proxy rather than a definitive measure of perception.

Applications include benchmarking animation pipelines, evaluating compression algorithms that preserve motion cues, and guiding user interface and virtual reality design toward settings with lower perceptual strain. Limitations include dependence on viewing conditions, display technology, and individual differences in perception, leading to ongoing discussion about cross-domain validity and standardization. Related concepts include visual perception, motion perception, and video quality assessment.