Automatic Image Aesthetic Assessment for Human-designed Digital Images

Recently, with the ever-growing scale of aesthetic assessment data, researchers have the image aesthetic assessment (IAA) task. Meanwhile, as technology developing, there are more and more human-designed digital images through software like Photoshop on the Internet. However, existing datasets merely focus on the images from real world, leaving the blank of aesthetic assessment of human-designed digital images. Adding to this, numerous existing IAA datasets rely solely on the Mean Opinion Score (MOS) for calculating aesthetic scores. Nonetheless, we contend that scores from individuals with diverse expertise should be treated distinctively, as differing fields of knowledge likely yield disparate opinions regarding the same image. To address these challenges, we construct the first Human-Designed Digital (HDDI) dataset for IAA tasks. And we develop a multi-angle method to generate aesthetic scores. Furthermore, we present the TAHF model as a novel baseline for our newly curated dataset. Empirical validation demonstrates the superior performance of our TAHF model over the current state-of-the-art (SOTA) model on the HDDI dataset.