针对直播摄像机调优，提出细粒度图像质量评估方法
TuningIQA: Fine-Grained Blind Image Quality Assessment for Livestreaming Camera Tuning
- 构建多属性标注的细粒度数据集FGLive-10K
- 在图像质量评分和排序上超越现有方法
- 适合需要精准调参的直播系统开发者
直播已成为现代视觉通信的重要形式，自动摄像机质量调优对提升用户体验至关重要。这需要准确的无参考图像质量评估（BIQA）来指导参数优化。然而，现有BIQA模型通常只提供粗略的整体质量评分，无法为精确调参提供细粒度感知指导。为此，我们建立了FGLive-10K，一个包含10,185张高分辨率图像的细粒度BIQA数据库，覆盖多种直播场景下不同摄像机参数配置。该数据集包含50,925个多重属性质量标注和19,234个细粒度成对偏好标注。基于此，我们进一步开发了TuningIQA，一种用于直播摄像机调优的细粒度BIQA度量，融合人类感知特征提取与基于图的摄像机参数融合。大量实验表明，TuningIQA在评分回归与细粒度质量排序任务中显著优于现有先进BIQA方法，并在实际直播调优部署中表现更优。
原文摘要 · Abstract (English)
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.
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