标题： TuningIQA：直播摄像机调校的细粒度盲图像质量评估 显示英文标题

标题： TuningIQA: Fine-Grained Blind Image Quality Assessment for Livestreaming Camera Tuning

摘要： 直播已经变得在现代视觉通信中越来越普遍，其中自动相机质量调校对于提供卓越的用户体验（QoE）至关重要。 这种调校需要准确的盲图像质量评估（BIQA）来指导参数优化决策。 不幸的是，现有的BIQA模型通常只预测一个总体的粗粒度质量分数，这无法为精确的相机参数调校提供细粒度的感知指导。 为了弥补这一差距，我们首先建立了FGLive-10K，一个全面的细粒度BIQA数据库，包含10,185张在不同相机参数配置下拍摄的高分辨率图像，涵盖了多种直播场景。 该数据集包含50,925个多属性质量注释和19,234个细粒度成对偏好注释。 基于FGLive-10K，我们进一步开发了TuningIQA，一种用于直播相机调校的细粒度BIQA度量，它结合了以人为本的特征提取和基于图的相机参数融合。 广泛的实验和比较表明，TuningIQA在分数回归和细粒度质量排名方面显著优于最先进的BIQA方法，在部署用于直播相机调校时表现出卓越的性能。 显示英文摘要

摘要： 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.