Explainable Image Quality Assessment

• Explainable IQA is a framework that pairs accurate quality predictions with human-readable explanations to bridge the gap between deep model outputs and human perception.
• It integrates traditional hand-crafted metrics and advanced deep learning with vision-language mappings to offer actionable insights into image distortions.
• This approach is crucial in domains like medical imaging and teledermatology, where transparent, quantifiable feedback supports informed decision-making.

Explainable Image Quality Assessment (IQA) refers to algorithmic frameworks that not only predict perceptual quality scores but also provide transparent, interpretable insights into the basis for their evaluations. In contrast to black-box IQA models that deliver only aggregate scores or opaque features, explainable IQA models incorporate methodologies and architectures designed to enhance interpretability, facilitate human understanding of quality judgments, and support practical deployment in applications where trust, traceability, or actionable feedback are essential. The contemporary landscape encompasses traditional hand-crafted metrics, deep and generative feature-based models, vision-language integrations, multi-task reinforcement learning, and instruction-tuned multimodal LLMs; each brings distinct approaches for making image quality reasoning interpretable.

1. Principles, Motivation, and Taxonomy

Explainable IQA emerges from the need to bridge the gap between algorithmic predictions and human visual perception, especially where model output must be understood, validated, or acted upon by domain experts (e.g., clinicians, photographers, or end-users). Central to its motivation is the recognition that conventional highly accurate deep models often act as black boxes, obscuring which aspects of an image contribute to their predictions, and limiting trust and actionable use.

The broad classes in IQA—full-reference (FR), reduced-reference (RR), blind/no-reference (NR)—are each amenable to explainable techniques. Key conceptual axes for explainability include:

• Hand-crafted transparency: Traditional metrics such as PSNR, SSIM, and their variants offer explicit mathematical interpretations, connecting quality drops to perceptual distortions via formulaic terms (e.g., luminance, contrast, structure).
• Architectural modularity: Models using filter-banks, center-surround mechanisms, and frequency scaling decompose signal artifacts in interpretable ways (e.g., S(i,j) = |C(i,j) – B(i,j)| elegantly formalizes local contrast differences).
• Multi-branch fusion: Unified frameworks that aggregate outputs from multiple algorithms or modalities allow attribution of quality judgment to well-defined perceptual cues.
• Vision-language mapping: Approaches leveraging VLMs or textual annotations integrate natural language or attribute-based descriptors as an explicit explainability layer.
• Saliency and visual reasoning: Saliency maps, chain-of-thought reasoning, and localized explanation pinpoint the image regions and features responsible for quality assessments.

Explainability thus comprises both algorithmic transparency and the ability to produce actionable, human-interpretable rationales for every quality prediction (You et al., 29 May 2024, Zhou et al., 14 Jun 2024, Li et al., 4 Oct 2025).

2. Architectures and Methodological Innovations

Modern explainable IQA models feature architectural designs explicitly oriented toward interpretability:

• Filter-bank Decomposition & Center-Surround: Frameworks decompose images into spatial-frequency sub-bands using banks of filters and then employ center-surround difference operators (e.g., S(i,j)), mimicking the human visual system’s local-to-global contrast analysis (Kottayil et al., 2017).
• Color Intensity Adaptation & Frequency Scaling: Models integrate color adaptation (e.g., adaptive weights w_R, w_G, w_B based on local statistics) and multi-scale frequency scaling (α(i,j)) to weight the contribution of each chromatic channel and frequency band in ways that reflect perceptual saliency.
• Unified Quality Indices: Fusing outputs from multiple traditional metrics via weighted summation, as in Q = ∑_k β_k * Q_k, creates hybrid indicators where each component can be explicated and visualized (Kottayil et al., 2017).
• Multi-scale Feature Fusion: Recent medical imaging models combine local (ResNet) and global (Swin Transformer) features, applying advanced channel attention mechanisms (e.g., Adaptive Graph Channel Attention) to merge anatomical context and low-level distortions, with stagewise feature visualization illuminating model focus (Li et al., 25 Jun 2025).
• Generative Models: Architectures grounded in generative autoencoders (e.g., VAE-QA) operate by fusing activation maps across layers for both reference and query images, enabling layerwise inspection and difference mapping of visually important features (Raviv et al., 28 Apr 2024).
• Vision–Language and Attribute-based Pipelines: Emerging approaches such as ExIQA use vision–LLMs (e.g., CLIP) to map images and natural language distortion attributes into a joint embedding space. Probability scores for each attribute (P̂(a|I)) provide explicit, interpretable pathways for quality assessment (Ranjbar et al., 10 Sep 2024).
• Reinforcement Learning and Chain-of-Thought: Multi-modal RL frameworks (e.g., Q-Insight) generate structured, human-readable reasoning chains as explanations, grounding both the numerical score and degradation classification in explicit model-generated narratives (Li et al., 28 Mar 2025).

3. Datasets, Experimental Protocols, and Evaluation

Explainable IQA research is underpinned by diverse and increasingly sophisticated datasets and evaluation metrics:

• Comprehensive Dataset Construction: Purpose-built datasets such as TADAC (over 1.6M images with content, distortion, and appearance textual annotations) (Zhou et al., 14 Jun 2024), DQ-495K (495K images with brief and detailed linguistic rationales) (You et al., 29 May 2024), EPAIQA-15K (partial-AIGC images with multidimensional human ratings and corresponding textual CoT explanations) (Qian et al., 12 Apr 2025), and PET-CT-IQA-DS (PET/CT images with radiologist scores) (Li et al., 25 Jun 2025) enable training and benchmarking of models for both quantitative and qualitative outputs.
• Standardized Performance Metrics: Common metrics include Spearman’s rank order correlation (SROCC), Pearson’s linear correlation (PLCC), and mean squared error (MSE) for score prediction; task-specific metrics include expert-level macro F1 (teledermatology) (Jalaboi et al., 2022), Pointing Game accuracy for saliency maps (Ozer et al., 2023), and coverage/diversity for instruction coreset selection (Li et al., 4 Oct 2025).
• Explainability Benchmarks and Protocols: Datasets such as Q-Bench and AesBench assess not only score prediction but also the quality of rationales, measuring model success at producing interpretable, reasoning-rich answers (quantified via GPT-4 scores for reasoning and human expert annotation agreement).
• Evaluation of Generalization: Cross-dataset and zero-shot testing on both real-world and synthetic distortions reveal models’ robustness and highlight whether explainability strategies improve transferability (Ranjbar et al., 10 Sep 2024, Liu et al., 3 Oct 2024).

4. Vision-Language and Human-Like Explanation

The adoption of vision-LLMs (VLMs) and multimodal LLMs (MLLMs) broadens the scope of explainable IQA:

• Descriptive and Comparative Explanation: Frameworks such as DepictQA-Wild (You et al., 29 May 2024) move beyond numeric scoring to generate brief distortion labels and detailed explanations in natural language, encompassing both assessment and comparison tasks. Structured outputs (JSON-like key-value mappings for observed distortions, content-dependent impact, and confidence scores) enable precise human interpretation.
• Attribute-based Distortion Identification: ExIQA (Ranjbar et al., 10 Sep 2024) predicts quality by detecting distortion attributes (e.g., “speckled noise,” “softening of details”) via CLIP-based image–text matching, and linearly composing attribute probabilities into distinguishable distortion strengths, clarifying which visual phenomena drive each score.
• Instruction Tuning and Data Selection: The scalability of explainable IQA with MLLMs depends strongly on the curation of instruction tuning datasets. IQA-Select’s clustering-based data selection (Li et al., 4 Oct 2025) demonstrates that smaller, highly informative coresets can produce higher explanation quality and better model performance, challenging the received “scaling law” in multimodal instruction fine-tuning.
• Chain-of-Thought Reasoning: RL-optimized models (e.g., Q-Insight (Li et al., 28 Mar 2025)) and EPAIQA’s CoT-enhanced LMMs output multi-step, reasoned explanations linking local and global image artifacts to overall quality, explicitly segmenting analyses into prompt adherence, local naturalness, and harmony—even for complex partial-AIGC scenarios (Qian et al., 12 Apr 2025).

5. Domain-Specific and Practical Applications

Explainable IQA methodologies are applied to a spectrum of specialized settings:

• Medical Imaging: Models targeting PET/CT and cardiac MRI employ explicitly structured, multi-scale, and attention-based fusion modules to ensure that both diagnostic artifacts and anatomical semantics are appraised and made visible via explainable outputs—critical for trust and adoption in clinical practice (Ozer et al., 2023, Li et al., 25 Jun 2025).
• Teledermatology: Lightweight CNNs such as ImageQX not only score image quality but give human-interpretable reasons (e.g., “bad framing,” “low resolution”) and local visualizations (via Grad-CAM), matching dermatologists in expert-level assessment and feedback (Jalaboi et al., 2022).
• AIGC and Localized Editing Assessment: EPAIQA models dissect and explain local editing effects (harmony, local naturalness, prompt completion), providing region-level diagnostic feedback and chain-of-thought explanation for each manipulation—a necessity in real-world AI-assisted editing pipelines (Qian et al., 12 Apr 2025).
• Zero-Shot and Training-Free Assessment: Recent standard-guided, segmentation-based MLLM frameworks (Dog-IQA (Liu et al., 3 Oct 2024)) combine object-level and global scoring to mimic human expert reasoning, achieving explainability and robust cross-domain generalization even without fine-tuning.

6. Challenges, Limitations, and Future Directions

Despite significant advances, open challenges persist:

• Balancing Accuracy and Interpretability: Deep models, especially Transformers and MLLMs, achieve state-of-the-art correlation with human judgment but risk diminished interpretability; combining modular, mathematically transparent components with deep features is an ongoing area of research (Ma et al., 12 Feb 2025).
• Dataset Curation and Domain Shift: The redundancy and potential domain bias in large instruction-tuning datasets can reduce performance and introduce explanation artifacts; data selection frameworks (e.g., IQA-Select) seek to overcome this but require further exploration (Li et al., 4 Oct 2025).
• Local vs. Global Causality: Disentangling the role of local artifacts vs. global semantic content remains an open technical challenge, particularly in multi-object, complex-scene, or highly edited images.
• Model Generalization and Zero-Shot Transfer: Ensuring explanation quality and prediction accuracy across diverse, unseen, and cross-modal domains is central to the field’s ongoing progress.
• Future Research Directions: Suggested avenues include deeper integration of human visual system principles, more principled hybridization of traditional and learned features, expanded use of linguistic and attribute rationales, and application to perceptual quality for video, 3D, or augmented reality content.

7. Summary Table: Key Explainable IQA Approaches

Method	Explainability Mechanism	Notable Attributes/Applications
Center-Surround (Kottayil et al., 2017)	Spatial decomposition, contrast mapping	Unified traditional cue integration
PIQ Library (Kastryulin et al., 2022)	Modular metric code, visual maps	Benchmarking & visual quality maps
VAE-QA (Raviv et al., 28 Apr 2024)	Generative feature fusion, difference maps	Layerwise inspection, memory efficiency
ExIQA (Ranjbar et al., 10 Sep 2024)	Vision-language attribute detection	Attribute-level explanations, zero-shot
DepictQA-Wild (You et al., 29 May 2024)	Vision-language reasoning, detailed descriptions	Task-unified, practical domain use
SLIQUE (Zhou et al., 14 Jun 2024)	Contrastive vision-language learning	TADAC dataset, semantic-appearance linkage
Q-Insight (Li et al., 28 Mar 2025)	RL-based, chain-of-thought reporting	Zero-shot, structured JSON explanations

Explainable IQA continues to evolve rapidly, with ongoing emphasis on combining accuracy, interpretability, and practical value—embedding inherently transparent mechanisms into full-system pipelines and aligning algorithmic judgments with the nuanced reasoning of human visual evaluators.