A Three-Feature Model to Predict Colour Change Blindness

Abstract

Change blindness is a striking shortcoming of our visual system which is exploited in the popular 'Spot the difference' game. It makes us unable to notice large visual changes happening right before our eyes and illustrates the fact that we see much less than we think we do. We introduce a fully automated model to predict colour change blindness in cartoon images based on two low-level image features and observer experience. Using linear regression with only three parameters, the predictions of the proposed model correlate significantly with measured detection times. We also demonstrate the efficacy of the model to classify stimuli in terms of difficulty.

High-Level Visual Masking of Image Compression Artefacts

“…• F9: Salience imbalance (between pristine and artefactridden stimuli) calculated as the Hamming distance between signature maps as in [17] and [18],…”

“…This indicates that the sample mean of decision times may not be the most representative statistics to analyse and predict our data. Consequently, we elected to use the mode, rather than the mean, to represent the distribution in the next section as in [18].…”