  In this paper, we introduce the problem of simultaneously detecting multiple
photographic defects. We aim at detecting the existence, severity, and
potential locations of common photographic defects related to color, noise,
blur and composition. The automatic detection of such defects could be used to
provide users with suggestions for how to improve photos without the need to
laboriously try various correction methods. Defect detection could also help
users select photos of higher quality while filtering out those with severe
defects in photo curation and summarization.
  To investigate this problem, we collected a large-scale dataset of user
annotations on seven common photographic defects, which allows us to evaluate
algorithms by measuring their consistency with human judgments. Our new dataset
enables us to formulate the problem as a multi-task learning problem and train
a multi-column deep convolutional neural network (CNN) to simultaneously
predict the severity of all the defects. Unlike some existing single-defect
estimation methods that rely on low-level statistics and may fail in many cases
on natural photographs, our model is able to understand image contents and
quality at a higher level. As a result, in our experiments, we show that our
model has predictions with much higher consistency with human judgments than
low-level methods as well as several baseline CNN models. Our model also
performs better than an average human from our user study.
