# No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation

arXiv: https://arxiv.org/abs/2008.08561 · HTML: https://mlsanity.com/paper/2008.08561/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2020.

## Built on by (most cited first, 17 of 17)

- [Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare](https://mlsanity.com/paper/2405.19298/) (2024)
- [No-Reference Point Cloud Quality Assessment via Domain Adaptation](https://mlsanity.com/paper/2112.02851/) (2021)
- [No-Reference Image Quality Assessment by Hallucinating Pristine Features](https://mlsanity.com/paper/2108.04165/) (2021)
- [Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessment](https://mlsanity.com/paper/2207.08124/) (2022)
- [StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessment](https://mlsanity.com/paper/2207.14489/) (2022)
- [Bridging the Synthetic-to-Authentic Gap: Distortion-Guided Unsupervised Domain Adaptation for Blind Image Quality Assessment](https://mlsanity.com/paper/2405.04167/) (2024)
- [Generalized Visual Quality Assessment of GAN-Generated Face Images](https://mlsanity.com/paper/2201.11975/) (2022)
- [Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment](https://mlsanity.com/paper/2209.05321/) (2022)
- [The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement](https://mlsanity.com/paper/2202.09738/) (2022)
- [Learning from Mixed Datasets: A Monotonic Image Quality Assessment Model](https://mlsanity.com/paper/2209.10451/) (2022)
- [Plug In, Grade Right: Psychology-Inspired AGIQA](https://mlsanity.com/paper/2512.22780/) (2025)
- [QD-PCQA: Quality-Aware Domain Adaptation for Point Cloud Quality Assessment](https://mlsanity.com/paper/2603.03726/) (2026)
- [Blind Image Quality Assessment: A Brief Survey](https://mlsanity.com/paper/2312.16551/) (2023)
- [From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training](https://mlsanity.com/paper/2501.13387/) (2025)
- [Towards Syn-to-Real IQA: A Novel Perspective on Reshaping Synthetic Data Distributions](https://mlsanity.com/paper/2601.00225/) (2026)
- [UPDA: Unsupervised Progressive Domain Adaptation for No-Reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2602.11969/) (2026)
- [Predictive Quality Assessment for Mobile Secure Graphics](https://mlsanity.com/paper/2509.20028/) (2025)

## Abstract (arXiv)

In this paper, we quest the capability of transferring the quality of natural scene images to the images that are not acquired by optical cameras (e.g., screen content images, SCIs), rooted in the widely accepted view that the human visual system has adapted and evolved through the perception of natural environment. Here, we develop the first unsupervised domain adaptation based no reference quality assessment method for SCIs, leveraging rich subjective ratings of the natural images (NIs). In general, it is a non-trivial task to directly transfer the quality prediction model from NIs to a new type of content (i.e., SCIs) that holds dramatically different statistical characteristics. Inspired by the transferability of pair-wise relationship, the proposed quality measure operates based on the philosophy of improving the transferability and discriminability simultaneously. In particular, we introduce three types of losses which complementarily and explicitly regularize the feature space of ranking in a progressive manner. Regarding feature discriminatory capability enhancement, we propose a center based loss to rectify the classifier and improve its prediction capability not only for source domain (NI) but also the target domain (SCI). For feature discrepancy minimization, the maximum mean discrepancy (MMD) is imposed on the extracted ranking features of NIs and SCIs. Furthermore, to further enhance the feature diversity, we introduce the correlation penalization between different feature dimensions, leading to the features with lower rank and higher diversity. Experiments show that our method can achieve higher performance on different source-target settings based on a light-weight convolution neural network. The proposed method also sheds light on learning quality assessment measures for unseen application-specific content without the cumbersome and costing subjective evaluations.