Sijing Wu

Image or video composition aims to combine the foreground object and the background content to generate a realistic composite image or video, in which harmonization algorithms play a crucial role in adjusting the color or light of the foreground object to make the entire image or video harmonious. However, existing image quality assessment (IQA) and video quality assessment (VQA) methods often fail to align with human preference in evaluating harmonization algorithms due to insensitivity to minor color or light inconsistencies. To address the issue and facilitate the advancement of harmonization, we introduce the first harmonization quality assessment database, termed HarmonyQAD, which consists of Harmony-IQAD comprising 1,800 harmonized images generated by 9 different image harmonizaion algorithms (IHAs), and HarmonyVQAD comprising 1,600 harmonized videos generated by 2 IHAs and 6 video harmonization algorithms (VHAs). All harmonized images and videos are accompanied by mean opinion scores (MOSs), collected through a meticulously designed subjective evaluation experiment. Based on the HarmonyQAD, we propose a unified framework for harmonization quality assessment, termed HarmonyQA, to predict the harmonization quality of both harmonized images and videos. Extensive experiments show that HarmonyQA achieves state-of-the-art performance on our datasets, and also achieves competing results on traditional IQA and VQA tasks. Furthermore, cross-dataset evaluation also shows that HarmonyQA exhibits better generalization ability than self-supervised learning-based IQA methods. The dataset and code are available at https://github.com/IntMeGroup/HarmonyQA.

Surveillance facial images are often captured under unconstrained conditions, resulting in severe quality degradation due to factors such as low resolution, motion blur, occlusion, and poor lighting. Although recent face restoration techniques applied to surveillance cameras can significantly enhance visual quality, they often compromise fidelity (i.e., identity-preserving features), which directly conflicts with the primary objective of surveillance images—reliable identity verification. Existing facial image quality assessment (FIQA) predominantly focus on either visual quality or recognition-oriented evaluation, thereby failing to jointly address visual quality and fidelity, which are critical for surveillance applications. To bridge this gap, we propose the first comprehensive study on surveillance facial image quality assessment (SFIQA), targeting the unique challenges inherent to surveillance scenarios. Specifically, we first construct SFIQA-Bench, a multi-dimensional quality assessment benchmark for surveillance facial images, which consists of 5, 004 surveillance facial images captured by three widely deployed surveillance cameras in real-world scenarios. A subjective experiment is conducted to collect six dimensional quality ratings, including noise, sharpness, colorfulness, contrast, fidelity and overall quality, covering the key aspects of SFIQA. Furthermore, we propose SFIQA-Assessor, a lightweight multi-task FIQA model that jointly exploits complementary facial views through cross-view feature interaction, and employs learnable task tokens to guide the unified regression of multiple quality dimensions. The experiment results on the proposed dataset show that our method achieves the best performance compared with the state-of-the-art general image quality assessment (IQA) and FIQA methods, validating its effectiveness for real-world surveillance applications. Both SFIQA-Bench and SFIQA-Assessor will be publicly released.

The rapid development of text-to-image (T2I) generation approaches has attracted extensive interest in evaluating the quality of generated images, leading to the development of various quality assessment methods for general-purpose T2I outputs. However, existing image quality assessment (IQA) methods are limited to providing global quality scores, failing to deliver fine-grained perceptual evaluations for structurally complex subjects like humans, which is a critical challenge considering the frequent anatomical and textural distortions in AI-generated human images (AGHIs). To address this gap, we introduce AGHI-QA, a large-scale benchmark specifically designed for quality assessment of AGHIs. The dataset comprises 4, 000 images generated from 400 carefully crafted text prompts using 10 state-of-the-art T2I models. We conduct a systematic subjective study to collect multidimensional annotations, including perceptual quality scores, text-image correspondence scores, visible and distorted body part labels. Based on AGHI-QA, we evaluate the strengths and weaknesses of current T2I methods in generating human images from multiple dimensions. Furthermore, we propose AGHI-Assessor, a novel quality metric that integrates the large multimodal model (LMM) with domain-specific human features for precise quality prediction and identification of visible and distorted body parts in AGHIs. Extensive experimental results demonstrate that AGHI-Assessor showcases state-of-the-art performance, significantly outperforming existing IQA methods in multidimensional quality assessment and surpassing leading LMMs in detecting structural distortions in AGHIs.