The prevalence of user-generated content (UGC) on platforms such as YouTube and TikTok has rendered no-reference (NR) perceptual video quality assessment (VQA) vital for optimizing video delivery. Nonetheless, the characteristics of non-professional acquisition and the subsequent transcoding of UGC video on sharing platforms present significant challenges for NR-VQA. Although NR-VQA models attempt to infer mean opinion scores (MOS), their modeling of subjective scores for compressed content remains limited due to the absence of fine-grained perceptual annotations of artifact types. To address these challenges, we propose CAMP-VQA, a novel NR-VQA framework that exploits the semantic understanding capabilities of large vision-language models. Our approach introduces a quality-aware prompting mechanism that integrates video metadata (e.g., resolution, frame rate, bitrate) with key fragments extracted from inter-frame variations to guide the BLIP-2 pretraining approach in generating fine-grained quality captions. A unified architecture has been designed to model perceptual quality across three dimensions: semantic alignment, temporal characteristics, and spatial characteristics. These multimodal features are extracted and fused, then regressed to video quality scores. Extensive experiments on a wide variety of UGC datasets demonstrate that our model consistently outperforms existing NR-VQA methods, achieving improved accuracy without the need for costly manual fine-grained annotations. Our method achieves the best performance in terms of average rank and linear correlation (SRCC: 0.928, PLCC: 0.938) compared to state-of-the-art methods. The source code and trained models, along with a user-friendly demo, are available at: https://github.com/xinyiW915/CAMP-VQA.

翻译：用户生成内容（UGC）在YouTube和TikTok等平台上的普及使得无参考（NR）感知视频质量评估（VQA）对于优化视频传输至关重要。然而，非专业采集的特性以及分享平台上UGC视频的后续转码为NR-VQA带来了显著挑战。尽管NR-VQA模型尝试推断平均意见分数（MOS），但由于缺乏对失真类型的细粒度感知标注，其对压缩内容主观分数的建模仍然有限。为应对这些挑战，我们提出了CAMP-VQA，一种利用大型视觉-语言模型语义理解能力的新型NR-VQA框架。我们的方法引入了一种质量感知提示机制，将视频元数据（如分辨率、帧率、码率）与从帧间变化中提取的关键片段相结合，以引导BLIP-2预训练方法生成细粒度的质量描述字幕。我们设计了一个统一架构，从三个维度建模感知质量：语义对齐、时序特性和空间特性。这些多模态特征被提取并融合，随后回归为视频质量分数。在多种UGC数据集上的广泛实验表明，我们的模型始终优于现有的NR-VQA方法，在无需昂贵人工细粒度标注的情况下实现了更高的准确性。与最先进方法相比，我们的方法在平均排名和线性相关性（SRCC：0.928，PLCC：0.938）方面取得了最佳性能。源代码、训练模型以及用户友好的演示可在以下网址获取：https://github.com/xinyiW915/CAMP-VQA。