Image quality assessment (IQA) of artificial intelligence-generated content (AIGC) has recently attracted significant research attention. Unlike general-purpose IQA, which primarily focuses on evaluating image content, AIGCIQA often requires addressing both the Text-to-Image (T2I) correspondence and the perceptual quality of images. To address this requirement, this paper proposes a novel two-stage AIGCIQA method. The first stage evaluates the alignment of the AI-generated images (AIGIs) with their corresponding descriptions, serving as an indicator of overall image quality. Specifically, positive and negative prompts are constructed to describe the T2I correspondence degree, and then a CLIP model is employed to predict the degree based on these image-prompt pairs. The second stage refines the perceptual quality assessment by integrating both global and local degradation features of AIGIs. Importantly, the contribution of local features is measured according to their correlation with the overall image, ensuring key regions are adequately represented in the quality prediction. Experimental results on AGIQA-1K, AGIQA-3K, and AIGCIQA2023 demonstrate the superior performance of the proposed method.

Recently, AI-generated images (AIGIs), synthesized based on initial textual prompts, have attracted widespread attention. However, due to limitations in current generation techniques, these images often exhibit degraded perceptual quality and semantic misalignment with the guiding prompts. Therefore, evaluating both perceptual quality and text-to-image alignment is essential for optimizing the performance of generative models. Existing methods design textual prompts solely based on the initial prompt for both perceptual and alignment quality tasks, and compute only coarse-grained similarity between the designed prompt and the generated image. However, such task-agnostic prompts overlook the distinctions between the perceptual and alignment quality tasks, and coarse-level similarity fails to capture semantic details, leading to suboptimal evaluation performance. To address these challenges, we propose a novel AIGI quality assessment framework, termed TPMS, which incorporates task-specific prompt and multi-granularity similarity computation. The task-specific prompt constructs dedicated prompts for perceptual and alignment quality respectively, allowing the model to capture distinct quality cues tailored to each evaluation task. Multi-granularity similarity measures the coarse-level similarity between the generated image and task-specific prompts to capture global quality characteristics, and the fine-level similarity between the generated image and the initial prompt to enhance semantic detail awareness. By integrating these two complementary similarities, TPMS enables precise and robust quality prediction. Extensive experiments on four widely-used AIGI quality benchmarks validate the effectiveness and superiority of the proposed framework.

The objective of blind image quality assessment (BIQA) is to develop a model capable of automatically evaluating image quality without requiring any reference knowledge. While multi-task learning has been widely utilized in BIQA, it has predominantly remained unimodal. This paper delves into the Visual-Language multi-task BIQA model, where distortion knowledge can be captured through image-text contrastive learning. Specifically, Visual-Language auxiliary tasks targeting distortion type and quality level are introduced, respectively, where both positive and negative image-text pairs are constructed for the target distorted image. Subsequently, image-text correspondences are learned in the embedding space while simultaneously evaluating image quality. Notably, in the auxiliary task learning, the proposed method not only brings the image and its corresponding positive text prompt closer but also pushes away the image from its negative text prompts, thereby facilitating the extraction of pertinent distortion features. In the quality assessment task, a patch-wise strategy is employed during the training phase. Differing from conventional BIQA methods, a novel NSS-guided quality weighting is introduced to gauge the correlation between patch quality and global quality, thereby enabling precise quality prediction. Extensive experiments are conducted on six IQA datasets, and the experimental results verify the superiority of the proposed method.

Image quality assessment (IQA) aims to build a quality prediction model to assess image quality automatically rather than artificially. Due to a lack of reference images, blind image quality assessment (BIQA) has become an attractive yet challenging research topic. Inspired by the hierarchical perception mechanism in the human visual system , some existing BIQA methods aggregate multi-stage features of a convolutional neural network (CNN). However, they are regardless of the latent dependencies. To solve this problem, we propose a novel BIQA method based on hierarchical dependency learning and quality aggregation (HDLaQA). The proposed method includes multi-stage feature extraction, hierarchical dependency learning, and quality aggregation. In multi-stage feature extraction, a CNN is used as the feature extractor and multi-stage features are output for further learning. In hierarchical dependency learning, spatial and channel dependencies among the multi-stage features are modeled. To this end, a dual-head spatial dependency (DSD) module is designed to harvest the spatial dependencies between the adjacent-stage features and deliver these dependencies to the next stage. Moreover, exponential bilinear pooling (EBP) is presented to learn the channel dependencies, which is more stable than commonly used BP. In quality aggregation, multiple quality scores are predicted based on the learned dependencies, and multiple learnable weights are used to measure the importance of the predicted scores for final quality evaluation. Experimental results on seven IQA databases demonstrate the competitiveness of the proposed method on both synthetic and authentic distortions.