Visual Quality Assessment of Composite Images: A Compression-Oriented Database and Measurement

Abstract

Composite images (CIs) have experienced unprecedented growth, especially with the prosperity of a large number of generative AI technologies. They are usually created by combining multiple visual elements from different sources to form a single cohesive composition, which have an increasing impact on a variety of vision applications. However, transmission of CIs can degrade their visual quality, especially undergoing lossy compression to reduce bandwidth and storage. To facilitate the development of objective measurements for CIs and investigate the influence of compression distortions on their perception, we establish a compression-oriented image quality assessment (CIQA) database for CIs (called ciCIQA) with 30 typical encoding distortions. Compressed with six representative codecs, we have carried out a large-scale subjective experiment that delivered 3,000 encoded CIs with labeled quality scores, making ciCIQA one of the earliest CI databases with the most compression types. ciCIQA enables us to explore the encoding effects on visual quality from the first five just noticeable difference (JND) points, offering insights for perceptual CI compression and related tasks. Moreover, we have proposed a new multi-masked no-reference CIQA method(called mmCIQA), including a multi-masked quality representation module, a self-supervised quality alignment module, and a multi-masked attentive fusion module. Experimental results demonstrate the outstanding performance of our mmCIQA in assessing the quality of CIs, outperforming 17 competitive approaches. The proposed method and database as well as the collected objective metrics are made publicly available on https://charwill.github.io/mmciqa.html.