AID-AppEAL: Automatic Image Dataset and Algorithm for Content Appeal Enhancement and Assessment Labeling

TL;DR

This paper introduces a new metric called Image Content Appeal Assessment (ICAA) that measures how positively an image's content engages viewers, distinct from aesthetic quality, and develops datasets and algorithms to automate appeal enhancement and evaluation.

Contribution

It is the first to explicitly study and quantify image content appeal separately from aesthetics, providing datasets, algorithms, and validation for appeal enhancement.

Findings

Over 76% user preference for appeal-enhanced images

Generated two large-scale datasets with 70K+ images each

Found little correlation between content appeal and aesthetics

Abstract

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.