Livestreaming has become increasingly prevalent in modern visual communication, where automatic camera quality tuning is essential for delivering superior user Quality of Experience (QoE). Such tuning requires accurate blind image quality assessment (BIQA) to guide parameter optimization decisions. Unfortunately, the existing BIQA models typically only predict an overall coarse-grained quality score, which cannot provide fine-grained perceptual guidance for precise camera parameter tuning. To bridge this gap, we first establish FGLive-10K, a comprehensive fine-grained BIQA database containing 10,185 high-resolution images captured under varying camera parameter configurations across diverse livestreaming scenarios. The dataset features 50,925 multi-attribute quality annotations and 19,234 fine-grained pairwise preference annotations. Based on FGLive-10K, we further develop TuningIQA, a fine-grained BIQA metric for livestreaming camera tuning, which integrates human-aware feature extraction and graph-based camera parameter fusion. Extensive experiments and comparisons demonstrate that TuningIQA significantly outperforms state-of-the-art BIQA methods in both score regression and fine-grained quality ranking, achieving superior performance when deployed for livestreaming camera tuning.

翻译：直播在现代视觉通信中日益普及，其中自动相机质量调优对于提供卓越的用户体验质量至关重要。此类调优需要准确的盲图像质量评估来指导参数优化决策。然而，现有的BIQA模型通常仅预测一个整体的粗粒度质量分数，无法为精确的相机参数调优提供细粒度的感知指导。为弥补这一差距，我们首先构建了FGLive-10K——一个包含10,185张高分辨率图像的综合性细粒度BIQA数据库，这些图像采集自多样化直播场景下不同相机参数配置。该数据集包含50,925个多属性质量标注和19,234个细粒度成对偏好标注。基于FGLive-10K，我们进一步开发了TuningIQA，一种面向直播相机调优的细粒度BIQA度量方法，该方法融合了人类感知特征提取与基于图的相机参数融合。大量实验与对比表明，TuningIQA在分数回归和细粒度质量排序方面均显著优于当前最先进的BIQA方法，在部署于直播相机调优时实现了卓越的性能。