Guiding Perception-Reasoning Closer to Human in Blind Image Quality Assessment

2025-12-18

AI Summary

This work addresses the lack of human-level perceptual reasoning and judgment consistency in blind image quality assessment (BIQA) models. To this end, we propose a perception–reasoning cascaded framework that explicitly models the human cognitive chain—“sensory input → implicit reasoning → quality judgment”—as a learnable, self-consistent reasoning path. We further introduce a reinforcement learning reward mechanism grounded in self-generated quality descriptions, balancing alignment with human preferences and internal logical consistency. Our method integrates human annotations, natural language generation, and ROUGE-1-based interpretability evaluation to achieve end-to-end interpretable BIQA. Experiments demonstrate state-of-the-art performance: highest Pearson and Spearman correlation coefficients among existing methods; ROUGE-1 score of 0.512—significantly surpassing the baseline (0.443)—validating high fidelity to human reasoning chains.

Technology Category

• Knowledge Representation and Reasoning: Qualitative Reasoning
• Computer Vision: Visual Reasoning & Symbolic Representations
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Develop a model for blind image quality assessment that mimics human perception-reasoning
• Use reinforcement learning with human annotations to guide human-like and self-consistent reasoning
• Achieve high performance in score prediction and alignment with human explanations

Innovation

• Using reinforcement learning with human reward signals
• Training model to infer quality from self-generated descriptions
• Achieving high human-model alignment via ROUGE-1 metric