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.

通过引入中性提示和多提示集成两个关键的组成部分，Q-Boost 方法在图像质量评估和视频质量评估任务中增强了低级多模态大语言模型在低级视觉问题方面的能力，并展示了优秀的零样本性能。