Does Color Influence Image Complexity Perception?

2020

Psychological studies explore visual complexity as perceived by humans. Image complexity is studied extensively in the mathematical, computational sciences. The two disciplines often define visual complexity differently and are thus disjointed. This is manifested in differences between subjective human-perceived complexity, and computer vision algorithms’ performance in visual tasks. Our study investigates this discrepancy in the context of cognitive tests that employ visual stimuli to assess a subject’s primal cognitive functions. A database of cognitive tests including visual recognition tasks and the performance of 403 subjects in terms of response times was used. Computer vision and information theory features were extracted from the images in these tasks. Inspired by cognitive psychology studies, the features were categorized into whole-image and object-specific features. A random forest classifier was used to map the computed features into three complexity labels in the tasks,...

International Journal of Affective Engineering, 2013

Perception of visual complexity in textures is very important for visual understanding and visual aesthetic evaluation. In this paper, we propose a new model of estimating subjective visual complexity perception of texture images. Compared with the traditional complexity measures based on information theory and fuzzy theory, the proposed model considers human visual perception, and it predicts the visual complexity of a texture corresponding to the subjective visual impression. Multiple linear regression (MLR) is used as a mapping function to map the relationship between the visual complexity perception and five texture characteristics including regularity, roughness, directionality, density and understandability. F-test and correlation analysis are applied to stimulated data and predicted data. The results of F-test (P < 0.01) prove that the proposed model can significantly predict the visual complexity of a texture, and the correlation coefficient between calculated complexity and subjective complexity (r = 0.951) of the testing textures shows that the results predicted by the proposed model are very close to the visual complexity judged by human subjects.

Humans are sensitive to complexity and

Optical Review, 2012

In our previous work we determined that five important characteristics affect the perception of visual complexity of a texture: regularity, roughness, directionality, density, and understandability. In this paper, a set of objective methods for measuring these characteristics is proposed: regularity is estimated by an autocorrelation function; roughness is computed based on local changes; directionality is measured by the maximum line-likeness of edges in different directions; and density is calculated from the edge density. Our analysis shows a significant correlation between the objective measures and subjective evaluations. In addition, for the estimation of understandability, a new approach is proposed. We asked the respondents to name each texture, and then we sorted all these names into different types, including names that were similar. We discovered that understandability is affected by two factors of a texture: the maximum number of similar names assigned to a specific type and the total number of types.

2004

Abstract Scenes are composed of numerous objects, textures and colors which are arranged in a variety of spatial layouts. This presents the question of how visual complexity is represented by a cognitive system. In this paper, we aim to study the representation of visual complexity for real-world scene images. Is visual complexity a perceptual property simple enough so that it can be compressed along a unique perceptual dimension? Or is visual complexity better represented by a multi-dimensional space?

Proceedings of the 8th International Conference on Engineering Psychology and Cognitive Ergonomics Held As Part of Hci International 2009, 2009

Visual complexity is conventionally defined as the level of detail or intricacy contained within an image. This paper evaluates different measures of complexity and the extent to which they may be compromised by a familiarity bias. It considers the implications with reference to measures of visual complexity based on users' subjective judgments and explores other metrics which may provide a better basis for evaluating visual complexity in icons and displays. The interaction between shading and complexity is considered as a future direction for the empirical study of visual complexity.

This paper reports the results of experiments conducted to determine whether a mathematical/computational model reflects the perception of complexity of background image scenes. Background scenes, such as landscapes, are composed of non-discrete image data. They contain repetitive patterns and colour schemes and therefore the images have to be described using pattern analysis techniques. A brief outline is given regarding the difference in composition of the foreground and background in images. Complexity measures and theories are then explored and results of a pilot test and experiment reported which correlate human perceptual data with a pattern measure which was devised by . This measure had previously only been tested on binary data and not on more realistic image data. The results show a very strong, positive correlation (r=0.899, p<0.01) which seems to suggest that the human perception of complexity of non-determinate image data is modelled well by the pattern measure. The paper concludes by outlining potential implications of the work and uses of the model.

2013

This study aims to explore visual complexity in streetscape composition using RMS contrast information and Fractal Analysis. The dataset was composed of 74 streetscape images, taken in Algeria and Japan in daytime and nighttime. The evaluation and analysis covered two phases: (1) Subjective quantification of perceived complexity using ranking method. (2) Visual complexity measurement based on RMS contrast statistics and fractal characteristics of the streetscape images. The results showed a positive correlation between the subjective ranking of complexity in streetscape images and the proposed measure of visual complexity "α" as well as with the fractal dimension D b .

2013 IEEE International Conference on Image Processing, 2013

In this paper, we propose a framework to assess visual complexity of paintings. This framework provides a machine learning scheme for investigating the relationship between human visual complexity perception and low-level image features. Since the global and local characteristics of paintings affect human's holistic impression and detail perception, we design a set of methods to extract the features that represent the global and local characteristics of paintings. By feature selection, we look into the role that each image feature plays in assessing visual complexity. Then the selected features are combined by a Support Vector Machine for classification. Experimental results indicate that the proposed work can predict the visual complexity perception of paintings with the accuracy of 88.13%, which is highly close to the assessments given by humans. Compared with the conventional measure of complexity, our approach considers human visual perception and performs more efficiently in assessing visual complexity of painting images.

2011 18th IEEE International Conference on Image Processing, 2011

Digital images can be analyzed at wide range of levels going from pixel arrangement to semantics. As a consequence, finding a visual complexity estimator is a difficult task. In this article we propose a definition of attention based perceptual complexity. We study the performance of human eye movements and of different models of computational attention against a ground-truth of image complexity based on the observation time of an image description task. The results obtained show that besides its lack of semantic processing, attentional behavior is a good estimator of image complexity.