The development of Large Language Models (LLM) and Diffusion Models brings
the boom of Artificial Intelligence Generated Content (AIGC). It is essential
to build an effective quality assessment framework to provide a quantifiable
evaluation of different images or videos based on the AIGC technologies. The
content generated by AIGC methods is driven by the crafted prompts. Therefore,
it is intuitive that the prompts can also serve as the foundation of the AIGC
quality assessment. This study proposes an effective AIGC quality assessment
(QA) framework. First, we propose a hybrid prompt encoding method based on a
dual-source CLIP (Contrastive Language-Image Pre-Training) text encoder to
understand and respond to the prompt conditions. Second, we propose an
ensemble-based feature mixer module to effectively blend the adapted prompt and
vision features. The empirical study practices in two datasets: AIGIQA-20K
(AI-Generated Image Quality Assessment database) and T2VQA-DB (Text-to-Video
Quality Assessment DataBase), which validates the effectiveness of our proposed
method: Prompt Condition Quality Assessment (PCQA). Our proposed simple and
feasible framework may promote research development in the multimodal
generation field.

研究提出了一种基于混合提示编码和基于集成的特征混合模块的 AIGC 质量评估框架，验证了方法在两个数据集上的有效性，从而促进了多模态生成领域的研究发展。

PCQA：基于提示条件的 AIGC 质量评估的强基准

PCQA: A Strong Baseline for AIGC Quality Assessment Based on Prompt  Condition

With the rapid advancements in AI-Generated Content (AIGC), AI-Generated
Images (AIGIs) have been widely applied in entertainment, education, and social
media. However, due to the significant variance in quality among different
AIGIs, there is an urgent need for models that consistently match human
subjective ratings. To address this issue, we organized a challenge towards
AIGC quality assessment on NTIRE 2024 that extensively considers 15 popular
generative models, utilizing dynamic hyper-parameters (including
classifier-free guidance, iteration epochs, and output image resolution), and
gather subjective scores that consider perceptual quality and text-to-image
alignment altogether comprehensively involving 21 subjects. This approach
culminates in the creation of the largest fine-grained AIGI subjective quality
database to date with 20,000 AIGIs and 420,000 subjective ratings, known as
AIGIQA-20K. Furthermore, we conduct benchmark experiments on this database to
assess the correspondence between 16 mainstream AIGI quality models and human
perception. We anticipate that this large-scale quality database will inspire
robust quality indicators for AIGIs and propel the evolution of AIGC for
vision. The database is released on
this https URL

通过考虑 15 个流行生成模型和动态超参数，本研究组织了一项挑战，全面评估人类主观评分、感知质量和文本图像对齐等方面，以创建迄今最大的细粒度 AIGI 主观质量数据库 AIGIQA-20K，并通过基准实验评估 16 个主流 AIGI 质量模型与人类感知之间的相应关系。预期该数据库将推动 AIGC 在视觉领域的进展。