Q-Boost: On Visual Quality Assessment Ability of Low-level
  Multi-Modality Foundation Models
Zicheng Zhang Haoning Wu Zhongpeng Ji Chunyi Li Erli Zhang Wei Sun Xiaohong Liu Xiongkuo Min Fengyu Sun Shangling Jui Weisi Lin Guangtao Zhai
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.