Abstract
With the emergence of large online datasets, research with digital versions of images is more popular than ever. However, little work has been done to assess the fidelity of these digital reproductions. We identify four types of fidelity loss in the WikiArt dataset and assess their effect on aesthetic judgement and originality assessment in two online studies. Our Fidelity Of Painting Images Dataset (FOPID) contains 750 images spanning 25 art styles, including 250 low-fidelity images sampled from WikiArt (blurred, cropped, enhanced, outdated, or combinations thereof), and two higher-fidelity counterparts, one from a museum website and one from a third-party source (with in-between quality). In Study 1, lay participants (N=298) rated 100 of the 250 images (randomly in one of the three versions) on their aesthetic appreciation (0–100 scale). No significant difference was found between fidelity groups , although images with combined distortions received the lowest mean rating. In Study 2, lay participants (N=108) saw the three variants of each painting side by side, and indicated their preferred image and the image they believed the original. The in-between version was most preferred, but the high-fidelity version was most often identified as the original. Overall, fidelity appears to matter little without context, although different distortions lead to different aesthetic evaluations. When different versions can be compared directly, participants prefer higher-fidelity images, calling into question the generalizability of studies relying on low-fidelity reproductions and underscoring the importance of fidelity awareness in aesthetics research.
Presenters
Lisa KoßmannPhD Candidate, Brain and Cognition, KU Leuven, Vlaams Brabant (nl), Belgium Johan Wagemans
Professor, Brain and Cognition, University of Leuven, Vlaams Brabant (nl), Belgium Stefanie De Winter
Post-doctoral Researcher, Art History, KU Leuven, Belgium
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Images, Fidelity, Quality, Digital, Reproduction, Originality, Painting, Dataset, Aesthetics, Appreciation
