---
title: 'FGLive-10K: Livestreaming BIQA Dataset'
url: https://www.emergentmind.com/topics/fglive-10k
type: topic
---

# FGLive-10K: Livestreaming BIQA Dataset

FGLive-10K is a fine-grained blind image quality assessment (BIQA) dataset built specifically for livestreaming camera tuning. It was introduced to address a limitation of conventional BIQA practice: existing datasets and models typically provide only a single coarse overall quality score, whereas livestreaming camera tuning requires supervision that can distinguish subtle but operationally important differences induced by parameter changes such as exposure, ISO, white balance, contrast, saturation, and sharpness. In the formulation accompanying TuningIQA, FGLive-10K couples overall MOS-style labels with fine-grained quality attributes and pairwise preference annotations so that models can learn not only how good an image is, but also why it is good or bad and which of two near-similar images is preferred for tuning decisions [2508.17965].

## 1. Definition and problem setting

FGLive-10K is described as the first dataset of its kind for livestreaming camera tuning [2508.17965]. Its design is motivated by two properties of livestreaming image quality assessment that differ from conventional IQA settings. First, livestreaming exhibits **human-region priority**: faces and bodies are usually the most important regions, and degradations affecting them can dominate perceptual quality judgments. Second, livestreaming exhibits **parameter interdependency**: camera controls are not independent, and exposure-related variables such as ISO, shutter speed, and aperture interact physically rather than additively [2508.17965].

Within this problem setting, score-only BIQA is treated as insufficiently granular. Two images may have nearly the same MOS, yet one may be preferable for optimization because of subtle differences in face clarity, exposure balance, or noise. FGLive-10K therefore supports not only regression against absolute quality labels but also fine-grained ranking through pairwise comparison. This suggests a shift from coarse perceptual assessment toward actionable perceptual supervision for camera parameter selection [2508.17965].

## 2. Dataset composition and capture structure

FGLive-10K contains **10,185 high-resolution images** at **1920 × 1080** resolution drawn from **555 distinct scenes** and captured under **systematic camera-parameter variations** [2508.17965]. The associated metadata considers **7 camera parameters**: **aperture**, **shutter speed**, **ISO**, **white balance**, **contrast**, **saturation**, and **sharpness** [2508.17965].

A metadata-rich subset, **FineLive-p**, is defined for explicit camera-parameter interdependence modeling. FineLive-p contains **5,559 training images** and **1,148 test images**, and includes the full 7-parameter camera metadata [2508.17965]. In the TuningIQA study, this subset is used specifically to examine the effect of camera-parameter-aware modeling rather than generic image-only BIQA.

The scene-level train/test partition is also explicit. FGLive-10K is split by scene into **451 training scenes** with **8,148 images** and **72,843 pairs**, and **104 test scenes** with **2,037 images** and **3,705 fine-grained pairs** [2508.17965]. Because the split is scene-based rather than image-based, it is intended to prevent leakage and to test generalization across unseen livestreaming scenarios.

## 3. Annotation schema and fine-grained supervision

FGLive-10K provides two principal forms of supervision: multi-attribute MOS annotations and pairwise fine-grained preference annotations [2508.17965].

For the MOS-style component, annotators assign discrete scores from **1 to 5** for **overall quality**, **face quality**, **sharpness**, **exposure**, and **noise**. After post-screening, **each image received at least 16 annotations**, yielding **50,925 multi-attribute quality annotations** in total [2508.17965]. The attribute semantics are explicitly defined as follows:

| Attribute | Meaning |
|---|---|
| Overall | Global perceptual quality |
| Face quality | Perceptual quality of human facial regions |
| Sharpness | Clarity and detail preservation |
| Exposure | Appropriateness of brightness and contrast |
| Noise | Absence of grain and visual artifacts |

These labels are more informative than a single coarse BIQA score because they identify which perceptual factor is affected. In a camera-tuning context, that distinction is operationally important: the preferred correction for poor exposure is not the same as the preferred correction for poor sharpness or excessive noise.

For subtle comparisons, FGLive-10K supplements MOS with pairwise annotation. The dataset contains **91,946 annotated pairs**, of which **19,234 pairs** were **refined through human pairwise comparison**; the paper states that **21%** of all pairs were refined in this way [2508.17965]. The preference construction rule is

$$
c_{pq} = \begin{cases} c_{pq}^*, & \text{if } \Delta s > 0.8, \\
\frac{1}{K}\sum_{k=1}^K \psi_k(I_p, I_q), & \text{if } \Delta s \leq 0.8,
\end{cases}
$$

where \(c_{pq}^* = \mathbb{I}(s_p > s_q)\) is the initial MOS-based preference, \(\Delta s = |s_p - s_q|\), \(\psi_k \in \{0, 0.5, 1\}\) encodes worse/equal/better judgment, and \(K\) is the number of pair annotations [2508.17965]. This mechanism explicitly distinguishes coarse MOS-derived preference from human-verified fine-grained preference.

## 4. Statistical characteristics and motivation evidence

The dataset analysis reported with FGLive-10K provides empirical support for the claim that fine-grained supervision is necessary [2508.17965]. The **overall quality distribution is fairly uniform**, indicating coverage across diverse quality levels. **Face quality correlates strongly with overall quality**, which is consistent with the human-region priority that motivates the dataset. By contrast, **sharpness**, **exposure**, and **noise** are **less correlated with each other**, suggesting that these factors are complementary and should be modeled separately [2508.17965].

A central observation concerns annotation reliability at different granularity levels. Annotator correlation with MOS is high on average, with **PLCC = 0.85**, but within narrow MOS tiers it declines sharply; in the **4.2–5.0** tier it drops to **PLCC = 0.24** [2508.17965]. This is used to justify pairwise refinement: when images are already of relatively high quality and differ only subtly, absolute scalar MOS becomes a weak supervisory signal for preference-sensitive optimization.

Taken together, these observations support two conclusions. First, human-centric perceptual structure matters disproportionately in livestreaming. Second, relative judgments are better suited than single-image MOS for small quality differences. A plausible implication is that FGLive-10K is not merely a larger BIQA corpus, but a dataset intended to alter the learning target itself—from coarse score prediction to decision-oriented quality modeling.

## 5. Role in TuningIQA

FGLive-10K is the empirical foundation for **TuningIQA**, a BIQA framework designed for livestreaming camera tuning with both **single-image mode** and **pairwise mode** [2508.17965]. TuningIQA integrates **Human-aware Feature Extraction (HFE)** and **Graph-based Camera Parameter Fusion (GCPF)**, and is trained with a multi-task objective combining regression and ranking.

HFE is designed to emphasize the most perceptually important regions, especially human subjects. Its pipeline uses **Faster R-CNN** to detect humans, a **Human Aesthetics Network** built on **EfficientNetV2-M** to extract multi-scale features, **ROI Align** to extract human-region features, nine spatial partitions centered around the human subject, and a **cross-attention** fusion between human-aware features and backbone features [2508.17965]. The partition update is expressed as

$$
\mathbf{F}_k = \Phi_k(\hat{\mathbf{M}}_k) + \hat{\mathbf{M}}_k,\quad k \in \{1,\ldots,9\},
$$

and the cross-attention layer as

$$
\mathbf{F}_q = \text{CrossAttention}(\mathbf{Q}, \mathbf{K}, \mathbf{V}) = \text{softmax}\left(\frac{\mathbf{Q}\mathbf{K}^T}{\sqrt{d_k}}\right)\mathbf{V},
$$

with \( \mathbf{Q} = \mathbf{F}_h \mathbf{W}_Q \), \( \mathbf{K} = \mathbf{F}_b \mathbf{W}_K \), and \( \mathbf{V} = \mathbf{F}_b \mathbf{W}_V \) [2508.17965].

GCPF is used when camera metadata is available, especially on FineLive-p. It defines a heterogeneous graph \(\mathcal{G} = (\mathcal{V}, \mathcal{E})\) with **8 nodes**: one visual node and seven parameter nodes corresponding to the camera settings [2508.17965]. The graph includes cross-modal links, **Exposure triangle** connections among **ISO**, **shutter speed**, and **aperture**, a **Post-processing chain** among **contrast**, **saturation**, and **sharpness**, and a **Color correlation** relation in which **white balance affects saturation perception** [2508.17965]. Reasoning is performed with two GAT layers,

$$
\mathbf{H}^{(1)} = \text{GAT}_1(\mathbf{X}, \mathcal{E}), \quad \mathbf{H}^{(2)} = \text{GAT}_2(\mathbf{H}^{(1)}, \mathcal{E}),
$$

with the final fused feature taken from the updated visual node [2508.17965].

The learning objective combines confidence-weighted regression and fine-grained ranking:

$$
\mathcal{L} = \lambda_{reg}\mathcal{L}_{reg} + \lambda_{rank}\mathcal{L}_{rank},
$$

with

$$
\lambda_{reg} : \lambda_{rank} = 1 : 2
$$

so that ranking is emphasized more strongly than regression [2508.17965]. This weighting is consistent with the dataset’s stated purpose: camera tuning depends heavily on choosing between near-similar alternatives.

## 6. Empirical results and practical tuning performance

On FGLive-10K, TuningIQA is reported to outperform state-of-the-art BIQA baselines in both score regression and fine-grained quality ranking [2508.17965]. The main comparison includes the following results:

| Method | SRCC | PLCC | FG-ACC |
|---|---:|---:|---:|
| TuningIQA (MobileNetV3-S) | 0.9308 | 0.9302 | 0.7065 |
| TuningIQA (ResNet-50) | 0.9385 | 0.9364 | 0.7284 |
| LIQE | 0.9235 | 0.9206 | 0.6533 |
| MUSIQ | 0.8662 | 0.8687 | 0.6861 |
| MT-A | 0.8821 | 0.8829 | 0.6223 |
| CLIP-IQA+ | 0.7056 | 0.7039 | 0.5570 |

The reported interpretation is that some baselines achieve reasonable regression but still perform poorly on **fine-grained ranking**, indicating that score prediction alone is not sufficient for camera tuning [2508.17965].

The attribute-wise results further support this point. TuningIQA leads across the reported dimensions with **Sharpness** at **SRCC 0.935**, **PLCC 0.963**, **FG-ACC 0.773**; **Noise** at **SRCC 0.805**, **PLCC 0.905**, **FG-ACC 0.746**; **Exposure** at **SRCC 0.897**, **PLCC 0.949**, **FG-ACC 0.776**; and **Face** at **SRCC 0.885**, **PLCC 0.880**, **FG-ACC 0.725** [2508.17965]. These values are used to argue that the human-aware design is especially effective for face-centric quality understanding.

Ablation evidence is also explicit. On **FGLive-10K**, adding **HFE** improves **SRCC: 0.8757 → 0.8917**, **PLCC: 0.9201 → 0.9267**, and **FG-ACC: 0.7258 → 0.7496**. On **FGLive-p**, adding **GCPF** improves **Baseline SRCC: 0.8206**, **with GCPF: 0.8552**, and **with GCPF + HFE: 0.8613**, with analogous gains in PLCC and FG-ACC [2508.17965]. This establishes a functional distinction between the two modules: HFE benefits the full dataset, whereas GCPF is useful when explicit parameter metadata is available.

Practical deployment relevance is assessed by simulating incremental parameter adjustments and using IQA metrics to select the best image. In a subjective comparison across **44 scenarios** with **12 volunteers**, TuningIQA achieves a **76% win rate vs. LIQE** and a **74% win rate vs. MUSIQ** [2508.17965]. This suggests that the dataset’s fine-grained annotation scheme supports not only offline benchmark gains but also better parameter-selection behavior in a tuning loop.

## 7. Scope, significance, and naming ambiguity

FGLive-10K is practically significant because it provides a dataset tailored to the requirements of livestreaming camera tuning: it captures real camera-parameter variations, includes human-centered perceptual attributes, adds pairwise fine-grained preference labels, supports both regression and ranking learning, and enables research on camera-parameter-aware quality modeling [2508.17965]. Within BIQA, it therefore occupies a specific niche at the intersection of perceptual modeling, camera control, and livestreaming QoE.

The resource also sharpens a broader methodological point. Traditional BIQA datasets usually provide one MOS score per image; FGLive-10K adds multi-attribute labels and pairwise preferences. This suggests a movement from static quality scoring toward supervision that can guide intervention. In the context of livestreaming, where tuning decisions are incremental and quality differences can be subtle, such supervision is structurally better aligned with the downstream task.

There is, however, a naming ambiguity in later arXiv usage. In "FIGMA: Towards FIne-Grained Music retrievAl" [2606.06615], the query-side term “FGLive-10K” is described as referring to the **FGMCaps 10K test split**, a **10,000-pair** evaluation set for fine-grained music retrieval rather than a livestreaming BIQA dataset. That paper states that this 10K split is built from **MTG-Jamendo**, **Music4All**, and **JamendoMaxCaps**, and is annotated with **Tempo**, **Key**, **Chord progression**, **Beat count**, **Genre**, and **Mood** for bidirectional text-audio retrieval evaluation [2606.06615]. This is a different resource and task setting. Accordingly, in current technical usage, the primary and unambiguous meaning of **FGLive-10K** in computer vision and IQA is the livestreaming camera-tuning dataset introduced with TuningIQA, while the music-retrieval usage reflects a separate query-context label rather than the same benchmark [2508.17965][2606.06615].

Source: https://www.emergentmind.com/topics/fglive-10k