Streetscapes are basic urban elements which play a major role in the livability of a city. The visual complexity of streetscapes is known to influence how people behave in such built spaces. However, how and which characteristics of a visual scene influence our perception of complexity have yet to be fully understood. This study proposes a method to evaluate the complexity perceived in streetscapes based on the statistics of local contrast and spatial frequency. Here, 74 streetscape images from four cities, including daytime and nighttime scenes, were ranked for complexity by 40 participants. Image processing was then used to locally segment contrast and spatial frequency in the streetscapes. The statistics of these characteristics were extracted and later combined to form a single objective measure. The direct use of statistics revealed structural or morphological patterns in streetscapes related to the perception of complexity. Furthermore, in comparison to conventional measures of visual complexity, the proposed objective measure exhibits a higher correlation with the opinion of the participants. Also, the performance of this method is more robust regarding different time scenarios.

This study represents an experimental research based on the electrophysiological evaluation of perceived complexity in streetscapes. Two physical measurements of perceived complexity, based on RMS contrast statistics and fractal information, were compared to human judgmental responses issued from brainwaves using Epoc Emotiv neuroheadset. The results indicated that frequency bands showed significant results in alpha and beta power bands in occipital and frontal electrodes, respectively. The higher the degree of familiarity with the streetscape image, the higher the degree of relaxation reflected by the increase of alpha power. Alpha power increased in dark streetscape images. RMS contrast statistics as well as fractal dimension values showed a positive correlation with beta band power associated with arousal and attention.

This study aims to explore the semantic attributes of a dataset composed of 74 streetscape images collected in Algeria and Japan. Authors collected and evaluated human judgmental responses using SD method as a tool of Kansei engineering in order to provide information about human perception of the visual attributes of the collected images. The research process included: (1) Semantic selection of bipolar adjective pairs. (2) Psychological evaluation of the visual stimuli using a questionnaire of 40 adjectives presented in French and Japanese. (3) Semantic evaluation of the participants' rating using factor analysis, cluster analysis and analysis of variance (ANOVA). The results showed that: (1) The semantic space was described by three independent axes which explained 79.80% of the variability. (2) Low correlation between the impressions of Algerian and Japanese participants in both Algerian and Japanese streetscapes. (3) Differences in the distribution of responses among the 7-point scale and also in the number of axes. And (4) Gender differences showed small differences in the axis of activity that represent less than 0.74% of explained variance.

This study aims to explore visual complexity in streetscape composition using a new method of measurement based on RMS contrast information. 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) the subjective quantification of visual complexity using factor analysis and ranking method. (2) The visual complexity measurement based on RMS contrast statistics of streetscape images. The results showed a positive correlation between the subjective ranking of complexity in streetscape ������� ���� ���� ��������� �������� ��� ������� ����������� ����� ����� ������������ ���� ����� ��� �������� ������������� ���� moderate in nighttime streetscapes. The sensitivity to complexity was higher in Algerian participants compared to Japanese participants.

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