Visuospatial complexity refers to the level of detail or intricacy present within a scene, takinginto account both spatial and visual properties of the dynamic scene or the place (e.g.moving images, everyday driving, video games and other immersive media). There havebeen several studies on measuring visual complexity from various viewpoints, e.g. marketing,psychology, computer vision and cognitive science. This research project aims atanalysing and evaluating different models and tools that have been developed to measurelow-level features of visuospatial complexity such as Structural Similarity Index measurement,Feature Congestion measurement of clutter and Subband Entropy measurement ofclutter. We use two datasets, one focusing on (reflectional) symmetry in static images,and another that consists of real-world driving videos. The results of the evaluation showdifferent correlations between the implemented models such that the nature of the sceneplays a significant role.