  The proliferation of videos collected during in-the-wild natural settings has
pushed the development of effective Video Quality Assessment (VQA)
methodologies. Contemporary supervised opinion-driven VQA strategies
predominantly hinge on training from expensive human annotations for quality
scores, which limited the scale and distribution of VQA datasets and
consequently led to unsatisfactory generalization capacity of methods driven by
these data. On the other hand, although several handcrafted zero-shot quality
indices do not require training from human opinions, they are unable to account
for the semantics of videos, rendering them ineffective in comprehending
complex authentic distortions (e.g., white balance, exposure) and assessing the
quality of semantic content within videos. To address these challenges, we
introduce the text-prompted Semantic Affinity Quality Index (SAQI) and its
localized version (SAQI-Local) using Contrastive Language-Image Pre-training
(CLIP) to ascertain the affinity between textual prompts and visual features,
facilitating a comprehensive examination of semantic quality concerns without
the reliance on human quality annotations. By amalgamating SAQI with existing
low-level metrics, we propose the unified Blind Video Quality Index (BVQI) and
its improved version, BVQI-Local, which demonstrates unprecedented performance,
surpassing existing zero-shot indices by at least 24\% on all datasets.
Moreover, we devise an efficient fine-tuning scheme for BVQI-Local that jointly
optimizes text prompts and final fusion weights, resulting in state-of-the-art
performance and superior generalization ability in comparison to prevalent
opinion-driven VQA methods. We conduct comprehensive analyses to investigate
different quality concerns of distinct indices, demonstrating the effectiveness
and rationality of our design.
