Dog-IQA: Standard-guided Zero-shot MLLM for Mix-grained Image Quality Assessment

TL;DR

Dog-IQA is a training-free, zero-shot image quality assessment method that leverages multimodal large language models to imitate human judgment, achieving state-of-the-art performance without training costs.

Contribution

It introduces a novel zero-shot IQA approach using MLLMs with a standard-guided inference pipeline, eliminating training expenses and improving out-of-distribution generalization.

Findings

Achieves state-of-the-art performance among training-free methods.

Performs competitively with training-based methods in cross-dataset tests.

Utilizes local and global image information for accurate quality scoring.

Abstract

Image quality assessment (IQA) serves as the golden standard for all models' performance in nearly all computer vision fields. However, it still suffers from poor out-of-distribution generalization ability and expensive training costs. To address these problems, we propose Dog-IQA, a standard-guided zero-shot mix-grained IQA method, which is training-free and utilizes the exceptional prior knowledge of multimodal large language models (MLLMs). To obtain accurate IQA scores, namely scores consistent with humans, we design an MLLM-based inference pipeline that imitates human experts. In detail, Dog-IQA applies two techniques. First, Dog-IQA objectively scores with specific standards that utilize MLLM's behavior pattern and minimize the influence of subjective factors. Second, Dog-IQA comprehensively takes local semantic objects and the whole image as input and aggregates their scores, leveraging local and global information. Our proposed Dog-IQA achieves state-of-the-art (SOTA) performance compared with training-free methods, and competitive performance compared with training-based methods in cross-dataset scenarios. Our code will be available at https://github.com/Kai-Liu001/Dog-IQA.