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Updated: Aug 7, 2026
08:12
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
An information theory analysis of visual complexity and dissimilarity
1i Department of Psychology, McGill University, 1205 Dr Penfield Avenue, Montr6al, QC H3A 1B1, Canada. don.donderi@mcgill.ca
Perception
|July 14, 2006
Summary
Subjective and objective image complexity correlate highly in marine charts. Image dissimilarity can be predicted using complexity and overlay image measures, supporting a vector space theory for image perception.
Area of Science:
- Computer vision
- Image processing
- Human-computer interaction
Background:
- Subjective image complexity is often assessed via magnitude estimation scaling.
- Objective image complexity can be quantified by compressed file size.
- Previous research indicates a strong correlation between subjective and objective complexity measures.
Purpose of the Study:
- To investigate the relationship between subjective and objective complexity in bitmap images.
- To determine if image dissimilarity can be predicted from complexity measures.
- To explore the predictive power of overlay image complexity on subjective complexity.
Main Methods:
- Magnitude estimation scaling for subjective complexity assessment.
- Compressed file size for objective complexity measurement.
- Analysis of marine electronic chart and radar images.
Main Results:
- A high correlation was found between subjective and objective complexity measures.
- Subjective image dissimilarity was predictable from individual and overlay image complexities.
- Subjective complexity of overlaid images correlated with individual image complexities and their dissimilarity.
Conclusions:
- Objective and subjective image complexity are closely related.
- Image dissimilarity perception aligns with complexity and overlay image characteristics.
- A Euclidean vector space model effectively represents image complexity and dissimilarity.
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