Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment
This addresses quality assessment for computer-generated images, which is incremental as it adapts existing statistical methods to a new domain.
The authors tackled the problem of no-reference image quality assessment for screen content images (SCIs), which lack natural scene statistics, by learning SCI-specific statistics and achieved promising performance with high generalization in cross-dataset evaluations.
The statistical regularities of natural images, referred to as natural scene statistics, play an important role in no-reference image quality assessment. However, it has been widely acknowledged that screen content images (SCIs), which are typically computer generated, do not hold such statistics. Here we make the first attempt to learn the statistics of SCIs, based upon which the quality of SCIs can be effectively determined. The underlying mechanism of the proposed approach is based upon the mild assumption that the SCIs, which are not physically acquired, still obey certain statistics that could be understood in a learning fashion. We empirically show that the statistics deviation could be effectively leveraged in quality assessment, and the proposed method is superior when evaluated in different settings. Extensive experimental results demonstrate the Deep Feature Statistics based SCI Quality Assessment (DFSS-IQA) model delivers promising performance compared with existing NR-IQA models and shows a high generalization capability in the cross-dataset settings. The implementation of our method is publicly available at https://github.com/Baoliang93/DFSS-IQA.