Assessing product-image quality is important in the context of online shopping. A high quality image that
conveys more information about a product can boost the buyer’s confidence and can get more attention.
However, the notion of image quality for product-images is not the same as that in other domains. The
perception of quality of product-images depends not only on various photographic quality features but also
on various high level features such as clarity of the foreground or goodness of the background etc. In this
paper, we define a notion of product-image quality based on various such features. We conduct a crowdsourced
experiment to collect user judgments on thousands of eBay’s images. We formulate a multi-class
classification problem for modeling image quality by classifying images into good, fair and poor quality based
on the guided perceptual notions from the judges. We also conduct experiments with regression using average
crowd-sourced human judgments as target. We compute a pseudo-regression score with expected average of
predicted classes and also compute a score from the regression technique. We design many experiments with
various sampling and voting schemes with crowd-sourced data and construct various experimental image
quality models. Most of our models have reasonable accuracies (greater or equal to 70%) on test data set.
We observe that our computed image quality score has a high (0.66) rank correlation with average votes
from the crowd sourced human judgments.