Q-Tacit: Image Quality Assessment via Latent Visual Reasoning

TL;DR

Q-Tacit introduces a novel latent space reasoning paradigm for image quality assessment, surpassing language-centric methods by injecting visual priors and calibrating reasoning trajectories, leading to more efficient and effective quality evaluation.

Contribution

It proposes a new latent reasoning approach for IQA that moves beyond natural language, integrating visual priors and calibration to enhance assessment accuracy and efficiency.

Findings

Effective latent reasoning with fewer tokens

Strong overall performance in quality assessment

Validates language is not the only suitable representation

Abstract

Vision-Language Model (VLM)-based image quality assessment (IQA) has been significantly advanced by incorporating Chain-of-Thought (CoT) reasoning. Recent work has refined image quality reasoning by applying reinforcement learning (RL) and leveraging active visual tools. However, such strategies are typically language-centric, with visual information being treated as static preconditions. Quality-related visual cues often cannot be abstracted into text in extenso due to the gap between discrete textual tokens and quality perception space, which in turn restricts the reasoning effectiveness for visually intensive IQA tasks. In this paper, we revisit this by asking the question, "Is natural language the ideal space for quality reasoning?" and, as a consequence, we propose Q-Tacit, a new paradigm that elicits VLMs to reason beyond natural language in the latent quality space. Our approach follows a synergistic two-stage process: (i) injecting structural visual quality priors into the latent space, and (ii) calibrating latent reasoning trajectories to improve quality assessment ability. Extensive experiments demonstrate that Q-Tacit can effectively perform quality reasoning with significantly fewer tokens than previous reasoning-based methods, while achieving strong overall performance. This paper validates the proposition that language is not the only compact representation suitable for visual quality, opening possibilities for further exploration of effective latent reasoning paradigms for IQA. Source code will be released to support future research.