In Praise of Artifice Reloaded: Caution With Natural Image Databases in Modeling Vision - Martinez-Garcia, Marina; Bertalmío, Marcelo; Malo, Jesús (Frontiers in Neuroscience, 2019)
Subjective image quality databases are a major source of raw data on how the visual system works in naturalistic environments. These databases describe the sensitivity of many observers to a wide range of distortions of different nature and intensity seen on top of a variety of natural images. Data of this kind seems to open a number of possibilities for the vision scientist to check the models in realistic scenarios. However, while these natural databases are great benchmarks for models developed in some other way (e.g. by using the well-controlled artificial stimuli of traditional psychophysics), they should be carefully used when trying to fit vision models. Given the high dimensionality of the image space, it is very likely that some basic phenomena are under-represented in the database. Therefore, a model fitted on these large-scale natural databases will not reproduce these under-represented basic phenomena that could otherwise be easily illustrated with well selected artificial stimuli. In this work we study a specific example of the above statement. A standard cortical model using wavelets and divisive normalization fitted to reproduce subjective opinion on a large image quality database fails to reproduce basic cross-masking. Here we outline a solution for this problem using artificial stimuli related to a more general model. Then, we show that the resulting model also has a competitive performance in the large-scale database. Our experiments with these artificial stimuli show that when using steerable wavelets, the conventional unit norm Gaussian kernels in divisive normalization should be multiplied by high-pass diagonal matrices to reproduce basic trends in masking. We report that basic visual phenomena may be misrepresented in subjectively-rated natural image databases. This is an additional argument in praise of artifice in line with (Rust&Movshon 05).