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

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 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.