TL;DR
This paper introduces TuningIQA, a novel fine-grained blind image quality assessment method and a comprehensive dataset, to improve camera parameter tuning in livestreaming by providing detailed quality guidance.
Contribution
The paper presents FGLive-10K, a large fine-grained BIQA dataset, and develops TuningIQA, a new metric that enhances camera tuning accuracy for livestreaming applications.
Findings
TuningIQA outperforms existing BIQA models in quality prediction.
FGLive-10K provides extensive annotations for diverse scenarios.
TuningIQA improves camera parameter tuning in livestreaming.
Abstract
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…
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