Guiding Perception-Reasoning Closer to Human in Blind Image Quality Assessment
Unlocking Human-Like AI in Image Quality Assessment
This paper introduces a novel framework for Blind Image Quality Assessment (BIQA) that aligns model reasoning with human judgments. It involves collecting detailed human annotations, using reinforcement learning with new reward functions, and introducing ROUGE-1 as a metric for human-model alignment. The model achieves competitive performance and significantly improved interpretability, marking a step towards human-like interpretable reasoning in BIQA.
Quantifiable Impact & Core Advantages
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ROUGE-1 Score
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Human Alignment Improvement
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Training Time
Deep Analysis & Enterprise Applications
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Humans assess image quality through a perception-reasoning cascade, integrating sensory cues with implicit reasoning to form self-consistent judgments. In this work, we investigate how a model can acquire both human-like and self-consistent reasoning capability for blind image quality assessment (BIQA). We first collect human evaluation data that capture several aspects of human perception-reasoning pipeline. Then, we adopt reinforcement learning, using human annotations as reward signals to guide the model toward human-like perception and reasoning. To enable the model to internalize self-consistent reasoning capability, we design a reward that drives the model to infer the image quality purely from self-generated descriptions. Empirically, our approach achieves score prediction performance comparable to state-of-the-art BIQA systems under general metrics, including Pearson and Spearman correlation coefficients. In addition to the rating score, we assess human-model alignment using ROUGE-1 to measure the similarity between model-generated and human perception-reasoning chains. On over 1,000 human-annotated samples, our model reaches a ROUGE-1 score of 0.512 (cf. 0.443 for baseline), indicating substantial coverage of human explanations and marking a step toward human-like interpretable reasoning in BIQA.
Blind image quality assessment (BIQA) aims to simulate how humans perceive and evaluate the visual quality of an image. To understand which visual features are extracted from the image in perception and how these features are logically integrated into an overall judgment, researchers have explored a wide range of computational approaches.
The proposed framework follows a two-stage learning paradigm that integrates human-guided reinforcement and self-consistent reasoning. This design allows the model to align its perceptual and reasoning behavior with human judgments while maintaining self-consistent reasoning capability under both image-based and caption-based conditions.
Our model achieves competitive performance under both image-based and caption-based conditions, offering a step toward interpretable, human-aligned BIQA.
In this work, we explored how to model a human-like and self-consistent BIQA system. We collected a new set of human perception-reasoning annotations and used them to guide the model toward human-aligned visual understanding. In parallel, we introduced a caption-based self-consistency objective that required the model to infer quality solely from its own generated descriptions, thereby strengthening its internal reasoning ability.
0.512
ROUGE-1 Score achieved by our model
Enterprise Process Flow
Image/Prompt Input
→
Perception & Reasoning (Stage 1)
→
Caption, Reasoning, Rating Output
→
Self-Consistent Reasoning (Stage 2)
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Final Quality Judgment
| Model Feature | Our Model | Baseline (Q-Insight) |
|---|---|---|
| ROUGE-1 Score | 0.512 | 0.443 |
| Explicit Reasoning Guidance | Yes (Human Annotations) | No |
| Self-Consistency Mechanism | Yes (Caption-based) | No (Score Optimization) |
Case Study: Aligning with Human Judgment
Our model demonstrates superior alignment with human perception-reasoning chains compared to baseline models. For instance, in detecting 'dark regions', Q-Insight incorrectly concluded no impact, while our model accurately identified them as quality-degrading factors, mirroring human judgment. This highlights our model's ability to capture fine-grained perceptual cues and higher-level conceptual factors like 'overall atmosphere'.
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Your AI Implementation Roadmap
A structured approach to integrating human-like AI assessment into your workflows.
Phase 1: Data Collection & Annotation
Gathering diverse human evaluation data capturing perception-reasoning aspects.
Phase 2: Model Training & Reinforcement Learning
Implementing dual-stage learning with human-guided and self-consistency rewards.
Phase 3: Validation & Interpretability Analysis
Assessing performance against benchmarks and human-model alignment using ROUGE-1.
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