  We propose Image Content Appeal Assessment (ICAA), a novel metric that
quantifies the level of positive interest an image's content generates for
viewers, such as the appeal of food in a photograph. This is fundamentally
different from traditional Image-Aesthetics Assessment (IAA), which judges an
image's artistic quality. While previous studies often confuse the concepts of
``aesthetics'' and ``appeal,'' our work addresses this by being the first to
study ICAA explicitly. To do this, we propose a novel system that automates
dataset creation and implements algorithms to estimate and boost content
appeal. We use our pipeline to generate two large-scale datasets (70K+ images
each) in diverse domains (food and room interior design) to train our models,
which revealed little correlation between content appeal and aesthetics. Our
user study, with more than 76% of participants preferring the appeal-enhanced
images, confirms that our appeal ratings accurately reflect user preferences,
establishing ICAA as a unique evaluative criterion. Our code and datasets are
available at https://github.com/SherryXTChen/AID-Appeal.
