Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation Models

• URL: http://arxiv.org/abs/2312.15300v1
• Date: Sat, 23 Dec 2023 17:02:25 GMT
• Title: Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation Models
• Authors: Zicheng Zhang, Haoning Wu, Zhongpeng Ji, Chunyi Li, Erli Zhang, Wei Sun, Xiaohong Liu, Xiongkuo Min, Fengyu Sun, Shangling Jui, Weisi Lin, Guangtao Zhai
• Abstract summary: We introduce Q-Boost, a strategy designed to enhance low-level MLLMs in image quality assessment (IQA) and video quality assessment (VQA) tasks. Q-Boost innovates by incorporating a middle ground' approach through $neutral$ prompts, allowing for a more balanced and detailed assessment. The experimental results show that the low-level MLLMs exhibit outstanding zeros-shot performance on the IQA/VQA tasks equipped with the Q-Boost strategy.
• Score: 80.79438689784958
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in Multi-modality Large Language Models (MLLMs) have demonstrated remarkable capabilities in complex high-level vision tasks. However, the exploration of MLLM potential in visual quality assessment, a vital aspect of low-level vision, remains limited. To address this gap, we introduce Q-Boost, a novel strategy designed to enhance low-level MLLMs in image quality assessment (IQA) and video quality assessment (VQA) tasks, which is structured around two pivotal components: 1) Triadic-Tone Integration: Ordinary prompt design simply oscillates between the binary extremes of $positive$ and $negative$. Q-Boost innovates by incorporating a `middle ground' approach through $neutral$ prompts, allowing for a more balanced and detailed assessment. 2) Multi-Prompt Ensemble: Multiple quality-centric prompts are used to mitigate bias and acquire more accurate evaluation. The experimental results show that the low-level MLLMs exhibit outstanding zeros-shot performance on the IQA/VQA tasks equipped with the Q-Boost strategy.

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