DPC-VQA: Decoupling Quality Perception and Residual Calibration for Video Quality Assessment

• URL: http://arxiv.org/abs/2604.12813v1
• Date: Tue, 14 Apr 2026 14:40:23 GMT
• Title: DPC-VQA: Decoupling Quality Perception and Residual Calibration for Video Quality Assessment
• Abstract summary: multimodal large language models (MLLMs) have shown promising performance on video quality assessment (VQA) tasks.<n>Adapting them to new scenarios remains expensive due to large-scale retraining and costly mean opinion score (MOS) annotations.<n>We propose DPC-VQA, a decoupling perception and calibration framework for video quality assessment.
• Score: 49.02765249211074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent multimodal large language models (MLLMs) have shown promising performance on video quality assessment (VQA) tasks. However, adapting them to new scenarios remains expensive due to large-scale retraining and costly mean opinion score (MOS) annotations. In this paper, we argue that a pretrained MLLM already provides a useful perceptual prior for VQA, and that the main challenge is to efficiently calibrate this prior to the target MOS space. Based on this insight, we propose DPC-VQA, a decoupling perception and calibration framework for video quality assessment. Specifically, DPC-VQA uses a frozen MLLM to provide a base quality estimate and perceptual prior, and employs a lightweight calibration branch to predict a residual correction for target-scenario adaptation. This design avoids costly end-to-end retraining while maintaining reliable performance with lower training and data costs. Extensive experiments on both user-generated content (UGC) and AI-generated content (AIGC) benchmarks show that DPC-VQA achieves competitive performance against representative baselines, while using less than 2% of the trainable parameters of conventional MLLM-based VQA methods and remaining effective with only 20\% of MOS labels. The code will be released upon publication.

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