Image Quality Metrics, Personality Traits, and Subjective Evaluation of Indoor Environment Images

Abstract

Adaptive lighting systems can be designed to detect the spatial characteristics of the visual environment and adjust the light output to increase visual comfort and performance. Such systems would require computational metrics to estimate occupants’ visual perception of indoor environments. This paper describes an experimental study to investigate the relationship between the perceived quality of indoor environments, personality, and computational image quality metrics. Forty participants evaluated the visual preference, clarity, complexity, and colorfulness of 50 images of indoor environments. Twelve image quality metrics (maximum local variation (MLV), spatial frequency slope (α), BRISQUE, entropy (S), ITU spatial information (SI), visual complexity (Rspt), colorfulness (M), root mean square (RMS) contrast, Euler, energy (E), contour, and fractal dimension) were used to estimate participants’ subjective evaluations. While visual clarity, visual complexity, and colorfulness could be estimated using at least one metric, none of the metrics could estimate visual preference. The results indicate that perceived colorfulness is highly correlated with perceived clarity and complexity. Personality traits tested by the 10-item personality inventory (TIPI) did not impact the subjective evaluations of the indoor environmental images. Future studies will explore the impact of target and background luminance on the perceived quality of indoor images.

Keywords

• indoor environment
• visual comfort
• image quality metrics
• personality traits
• smart lighting systems
• building automation