CAP-IQA: Context-Aware Image Quality Assessment

• The paper introduces CAP-IQA, a framework integrating context and dynamic prompt engineering with multimodal fusion to enhance IQA accuracy and interpretability.
• It uses task-specific, contextual, and instance-aware prompts combined with cross-modal attention to better align quality predictions with human perception.
• The approach demonstrates robust performance on AI-generated, medical, and natural images, achieving high SRCC/PLCC metrics and improved adaptability across scenarios.

Context-Aware Prompt-guided Image Quality Assessment (CAP-IQA) is a research paradigm and technical framework wherein image quality assessment (IQA) models are explicitly conditioned on context and task semantics via carefully constructed prompts, enabling refined, adaptive, and interpretable predictions across diverse perceptual, semantic, and downstream requirements. CAP-IQA subsumes both traditional prompt-guided IQA (task- and metric-centric) and modern context-driven scenarios, integrating language-based priors, task-specific instructions, or instance-level features into the assessment pipeline to better align with human judgment and real-world heterogeneity.

1. Conceptual Foundations

Conventional no-reference and even multimodal IQA models generally operate with fixed, global mappings from image (and possibly text prompt) to scalar quality score, often disregarding the actual user intent, downstream task, or nuanced semantic relationships when assessing synthetic, medical, or application-driven images. CAP-IQA transcends this limitation by formalizing assessment as a function not only of the observed image (and optional reference image), but also of context: prompt $t$ that encodes required criteria, domain knowledge, or use-case (Xia et al., 2024, Qu et al., 2024, Rifa et al., 4 Jan 2026, Xun et al., 25 Jul 2025, Wang et al., 2024).

Key to CAP-IQA is the construction of prompt architectures that go beyond shallow mappings:

• Task-specific prompts: Separate textual encapsulations of perceptual fidelity, alignment to textual description, or semantic content (Xia et al., 2024).
• Contextual prompts: Incorporation of domain-, modality-, or scenario-specific information, relevant for medical or cross-modal benchmarks (Xun et al., 25 Jul 2025, Rifa et al., 4 Jan 2026).
• Instance-aware prompts: Adaptation of prompts at the instance level—image-dependent dynamic content tokens or context vectors that encode actual artifacts or acquisition context (Rifa et al., 4 Jan 2026).
• Instructional and ranking prompts: Integration with stepwise instructions, in-context examples, or comparative queries, as exploited in agentic and LLM-driven frameworks (Wu et al., 2024, Zhu et al., 30 Sep 2025).

The explicit modeling of prompt–context interactions affords CAP-IQA considerable generalization and interpretability compared to earlier DNN-based or unimodal methods.

2. Methodological Frameworks and Architectural Variants

Multiple architectural typologies have been proposed for CAP-IQA, leveraging combinations of vision-LLMs (VLMs), DNN-based quality nets, large multimodal models, and fusion backbones. Core methodologies include:

• Task-Specific Prompt & Multi-Granularity Similarity (TSP-MGS):
  • Task-specific prompts are generated to describe alignment and perception quality independently.
  • Multi-level similarity measures: coarse-grained (sentence-level, both global and patch) and fine-grained (word-level similarity between initial prompt and image) are computed via CLIP encoders, with adaptive weighting yielding the final score (Xia et al., 2024).
• Multimodal Feature Fusion:
  • Most approaches (e.g., IP-IQA, MP-IQE) extract parallel visual and textual features and fuse them via cross-attention or transformer blocks (Qu et al., 2024, Pan et al., 2024).
  • MA-AGIQA introduces mixture-of-experts (MoE) gating to combine visual DNN quality features and large multimodal model (LMM) semantic vectors under guiding prompts (Wang et al., 2024).
• Dynamic Context Integration:
  • MedIQA and CAP-IQA-CT frameworks encode context (modality, anatomic region, acquisition type) with one-hot prompts or radiology texts, injected at every transformer or encoder stage (Xun et al., 25 Jul 2025, Rifa et al., 4 Jan 2026).
  • Context-aware prompt fusion is realized via cross-attention or dynamic prompt modules, tailored to either global priors or sample-specific tokens.
• Agentic and LLM-driven Systems:
  • Modular agentic systems (AgenticIQA) decompose IQA into planning (strategy generation based on prompt and sample), execution (tool selection and application), and explanation generation, coordinated by a VLM backbone responsive to prompt-based input (Zhu et al., 30 Sep 2025).
  • LLM-based prompting strategies integrate psychophysical testing paradigms (single/double/multi-stimulus) and contextually rich prompts (in-context, chain-of-thought) to elicit human-aligned quality judgments (Wu et al., 2024).

3. Prompt Construction and Context Encoding

Prompt engineering is central to CAP-IQA. Several patterns have emerged:

Prompt Type	Example Construction	Contextual Role
Alignment-specific	"A photo that {adv} matches {pt}", adv ∈ {badly,…,perfectly}	Isolates text-to-image correspondence (Xia et al., 2024)
Perception-specific	"A photo of {adj} quality", adj ∈ {bad, poor, fair, ...}	Encodes perceived image fidelity (Xia et al., 2024)
Semantic-content	"Evaluate whether image quality is compromised due to {aspect}"	Focuses on fundamental semantic coherence/existence (Wang et al., 2024)
Domain/contextual	One-hot encodings of modality/region/type; Radiology-style definitions	Incorporates medical or scene context (Xun et al., 25 Jul 2025, Rifa et al., 4 Jan 2026)
Instance-level	Dynamic MLP-tokenized prompt vectors from pooled image features	Captures real, image-specific degradations (Rifa et al., 4 Jan 2026)
Instruction/CoT	Stepwise or example-based prompt composition for LLM/agentic models	Guides higher-order reasoning or explicit scoring (Wu et al., 2024)

Prompts are processed by text encoders (CLIP, BERT, PubMedBERT, LMMs), mapped into feature space, and interfaced with the visual encoder via cross-modal fusion (attention mechanisms, concatenation, or gating).

4. Quality Prediction Algorithms and Fusion

CAP-IQA models combine prompted features and visual evidence at various levels:

• Multi-granularity fusion: Sentence- and word-level similarities are integrated with image patches and global features; balancing weights (fixed or learned) control the importance of alignment vs. perception vs. fine detail (Xia et al., 2024).
• Mixture-of-experts (MoE) fusion: Each expert (e.g. DNN quality, semantic existence, coherence) is projected into a common representation, with gating weights learned per instance to produce the final score (Wang et al., 2024).
• Attention-based fusion: Cross-modal attention aligns text and vision features, with special tokens (e.g. [QA]) or learnable deep visual/text prompts steering the fusion (Qu et al., 2024, Pan et al., 2024).
• Regression and scoring: Most frameworks predict continuous mean opinion scores (MOS), occasionally decomposed into separate perception and alignment components (AGIQA-3K) (Xia et al., 2024, Qu et al., 2024).
• Losses: Mean absolute error (MAE), mean squared error (MSE), or smooth L1 loss are used, typically with additional alignment or cross-entropy objectives for auxiliary supervision.

Ablation studies demonstrate that omitting task-specific prompting, context-aware features, or fine-grained alignment mechanisms degrades both correlation coefficients (SRCC/PLCC) and cross-dataset robustness (Xia et al., 2024, Pan et al., 2024, Wang et al., 2024).

5. Benchmarks, Results, and Practical Performance

CAP-IQA models have been evaluated across standard and novel IQA datasets:

• AI-generated image data: AGIQA-1K, AGIQA-3K (perception and alignment scores separately) where TSP-MGS and MA-AGIQA set new state-of-the-art (SRCC up to 0.8939, PLCC up to 0.9273) (Xia et al., 2024, Wang et al., 2024).
• Natural scene and cross-domain data: PromptIQA, GenzIQA, and multimodal prompting approaches achieve robust performance and few-/zero-shot adaptability with mean SRCC/PLCC approaching 0.92–0.93 (Chen et al., 2024, De et al., 2024).
• Medical imaging: CAP-IQA and MedIQA demonstrate leading SRCC/PLCC (up to 0.87) and downstream generalization across modalities (CT, MRI, fundus), often outperforming Transformer and CNN baselines by significant margins (Rifa et al., 4 Jan 2026, Xun et al., 25 Jul 2025).
• Agentic and LLM settings: AgenticIQA shows competitive scoring accuracy (SRCC/PLCC up to 0.9165/0.9215 on TID2013), interpretable plan/execution/summarization, and adapts dynamically to user prompt requirements (Zhu et al., 30 Sep 2025). Multimodal LLMs exploit human psychophysical protocols but are currently limited by color and ranking discrimination (Wu et al., 2024).

Qualitative analysis demonstrates superior interpretability (human-readable rationale, attention maps indicating causality of score) and modular adaptability (prompt change suffices for domain transfer, no fine-tuning required in PromptIQA).

6. Challenges, Limitations, and Future Directions

Several open problems and research directions remain:

• Prompt design and adaptation: Fixed or hand-crafted prompts may inadequately reflect real-world variability or new use-cases; automated, dynamic, or reinforcement-learned prompt generation is indicated as a necessity (Xia et al., 2024, Xun et al., 25 Jul 2025).
• Robustness to non-structural degradations: Subtle errors (color, lighting, scanner noise) and prompt ambiguity can elude current VLM-based models (Xia et al., 2024, Xun et al., 25 Jul 2025).
• Causal and debiasing strategies: Explicitly disentangling prompt prior (“idealized” knowledge) from factual, image-specific evidence remains an unsolved challenge; recent work proposes context-token proxies and dynamic cross-prompt attention, but lacks formal causal graph treatment (Rifa et al., 4 Jan 2026).
• Scalability across modalities: While CAP-IQA generalizes across several domains, extending to volumetric, temporal, or truly interactive scenarios (e.g. video, streaming, user feedback) requires advanced context modeling (Xun et al., 25 Jul 2025, Zhu et al., 30 Sep 2025).

Planned improvements include learnable 3D context-prompt tuning, semi-supervised learning via unlabelled data, and further integration with downstream clinical/pathological pipelines (Xun et al., 25 Jul 2025, Rifa et al., 4 Jan 2026).

7. Significance and Impact

The emergence and rapid evolution of CAP-IQA frameworks has fundamentally shifted the landscape of automated image quality assessment. Enabling context- and prompt-sensitive modeling allows IQA systems to:

• Achieve higher alignment with subjective human perception and task requirements, especially in synthetic, domain-specific, or dynamic visual environments.
• Facilitate explainability and interpretability, with transparent rationale traceable to prompt design and fused evidence streams.
• Generalize beyond the scope of fixed, monolithic scorers—adapting to novel domains via prompt adaptation without dataset expansion or retraining (Chen et al., 2024, Xia et al., 2024, Zhu et al., 30 Sep 2025).

CAP-IQA now defines the state-of-the-art for benchmark performance in AI-generated imagery, medical diagnostics, and generalized multimodal IQA, establishing a foundation for further research into causal, semantic, and agentic quality assessment systems.