Q-Ponder: A Unified Training Pipeline for Reasoning-based Visual Quality Assessment

TL;DR

Q-Ponder introduces a unified training framework for multimodal large language models to improve both visual quality score regression and interpretability through a two-stage process involving distillation and reinforcement learning.

Contribution

It proposes a novel two-stage training pipeline with a reward-based fine-tuning method, achieving state-of-the-art results in visual quality assessment and interpretability.

Findings

Q-Ponder achieves up to 6.5% higher SRCC on cross-domain benchmarks.

It outperforms existing description-based models in accuracy and reasonableness.

The framework demonstrates strong generalization across diverse visual quality tasks.

Abstract

Recent studies demonstrate that multimodal large language models (MLLMs) can proficiently evaluate visual quality through interpretable assessments. However, existing approaches typically treat quality scoring and reasoning descriptions as separate tasks with disjoint optimization objectives, leading to a trade-off: models adept at quality reasoning descriptions struggle with precise score regression, while score-focused models lack interpretability. This limitation hinders the full potential of MLLMs in visual quality assessment, where accuracy and interpretability should be mutually reinforcing. To address this, we propose a unified two-stage training framework comprising a cold-start stage and a reinforcement learning-based fine-tuning stage. Specifically, in the first stage, we distill high-quality data from a teacher model through expert-designed prompts, initializing reasoning capabilities via cross-entropy loss supervision. In the second stage, we introduce a novel reward with Group Relative Policy Optimization (GRPO) to jointly optimize scoring accuracy and reasoning consistency. We designate the models derived from these two stages as Q-Ponder-CI and Q-Ponder. Extensive experiments show that Q-Ponder achieves state-of-the-art (SOTA) performance on quality score regression benchmarks, delivering up to 6.5% higher SRCC on cross-domain datasets. Furthermore, Q-Ponder significantly outperforms description-based SOTA models, including its teacher model Qwen-2.5-VL-72B, particularly in description accuracy and reasonableness, demonstrating the generalization potential over diverse tasks.