---
title: 'HFR-LS: High Frame Rate Live Streaming Dataset'
url: https://www.emergentmind.com/topics/high-frame-rate-live-streaming-hfr-ls-dataset
type: topic
---

# HFR-LS: High Frame Rate Live Streaming Dataset

The High Frame Rate Live Streaming (HFR-LS) Dataset is a subject-rated video database that systematically explores the perceptual trade-offs between frame rate (temporal fidelity) and compression (spatial fidelity) under fixed bitrate constraints, reflecting realistic encoding scenarios in live video streaming. Designed for benchmarking perceptual quality, the HFR-LS dataset supports the development and evaluation of both encoding schemes and video quality assessment (VQA) models for bitrate-constrained high-frame-rate applications [2601.19776].

## 1. Scope, Content, and Encoding Paradigm

HFR-LS is constructed from 32 source video sequences, each 5 seconds in duration, originally captured at 120 frames per second (fps) in YUV 4:2:0 8-bit format and downscaled to 1920×1080 resolution. Sources are drawn from BVI-HFR (22 sequences), UVG (5 sequences), and LIVE-YT-HFR (5 sequences), yielding diverse content with broad coverage in spatial information (SI) and temporal information (TI), per ITU-T P.910 recommendations. Approximately one-third of sequences (12/32) contain camera motion, while others vary from low to high temporal complexity.

Each source video is re-encoded using the H.264 codec (FFmpeg preset=fast, tune=zerolatency) at fixed 1080p resolution via four discrete target bitrates—5, 7, 10, and 15 Mbps—and three frame rates—30, 60, and 120 fps—with frame-rate reduction realized through frame dropping. This encoding "ladder" results in 32 × (4 × 3) = 384 processed video clips. The dataset is organized to represent prototypical "live streaming ladder" choices as recommended by leading streaming platforms (e.g., YouTube, Twitch, Facebook Live) [2601.19776].

## 2. Subjective Evaluation Methodology

The subjective quality evaluation is conducted under a single-stimulus, hidden-reference protocol conforming to ITU-R BT.500-15. All 384 processed clips, together with their respective 120 fps/maximum-quality reference, are presented in randomized order on a calibrated 120 Hz, 1080p monitor. Thirty naïve observers (12 female, 18 male; age 20–30; normal or corrected vision) participate, with each subject rating all videos independently on a continuous 0–100 quality scale.

Sessions (~2,080 seconds total video) are divided into two blocks separated by a 5-minute break to mitigate observer fatigue. A brief training phase familiarizes each subject with the protocol. Outlier scores are identified and removed according to ITU-R BT.500 guidelines. Each subject’s scores $s_{ij}$ (for subject $i$ on clip $j$) are normalized to z-scores:

$$z_{ij} = \frac{s_{ij} - \mu_i}{\sigma_i}$$

Linearly rescaled:

$$z'_{ij} = 100 \cdot \frac{z_{ij} + 6}{9}$$

The mean opinion score (MOS) for each clip is:

$$\mathrm{MOS}_j = \frac{1}{N} \sum_i z'_{ij}$$

Difference MOS (DMOS) is reported relative to the hidden 120 fps reference:

$$\mathrm{DMOS}_j = \mathrm{MOS}_\mathrm{ref} - \mathrm{MOS}_j$$

Inter-subject consistency is high (median Pearson $r\approx0.91$ over random splits), indicating reliable perceptual data.

## 3. Data Organization and Accessibility

The dataset is hosted at [https://github.com/real-hjq/HFR-LS](https://github.com/real-hjq/HFR-LS) and includes:

- `videos/`
  - `source/`: 32 uncompressed YUV420p reference sequences at 120 fps.
  - `processed/`: 384 H.264 MP4s, named `SRCXX_BRYY_FRZZ.mp4` (`XX`=seq. ID, `YY`=bitrate Mbps, `ZZ`=frame rate).
- `metadata.csv`: Contains source ID, bitrate (Mbps), frame rate (fps), SI, TI, MOS, DMOS for each processed clip.
- `dmosaes/`: 384×1 vector of DMOS scores, ordered by clip.
- `scripts/`: Python tools for metadata parsing, objective metric computation, and figure regeneration (with `requirements.txt`).
- Download: `git clone https://github.com/real-hjq/HFR-LS.git`
- Usage: Load metadata (`python load_metadata.py`), evaluate reference models (`python eval_vqa.py`).

Researchers can directly access the quality-annotated processed clips, metadata, and analysis scripts for benchmarking and custom VQA model development.

## 4. Perceptual Findings and Statistical Analysis

The subjective study exposes a statistically significant main effect of frame rate on perceived quality (one-way ANOVA $p=4.47\times10^{-4}$). Significant interactions are found between bitrate and frame rate (two-way ANOVA $p=4.15\times10^{-5}$), and between source content and frame rate ($p=4.79\times10^{-3}$).

Key empirical results:

- At low bitrates (5 Mbps), high frame rates (120 fps) introduce pronounced compression artifacts, degrading quality.
- For sequences with camera motion ($\mathrm{TI}>6$), 60 fps outperforms 30 fps at moderate bitrates. 120 fps only surpasses 60 fps at bitrates above ~12 Mbps.
- For static or low-motion content ($\mathrm{TI}<6$), frame rate differentials remain minor until bitrates exceed ~10 Mbps, where 60 fps and 120 fps offer modest subjective gains.

These findings clarify that optimal bitrate allocation between spatial and temporal fidelity should be content-aware, especially under strict bandwidth budgets.

## 5. Objective VQA Metrics and Benchmarking

Full-reference objective VQA metrics computed on the dataset include PSNR, SSIM, LPIPS, DISTS, and VMAF, accompanied by no-reference metrics such as NIQE, VSFA, Li22, DOVER, ModularVQA, and MinimalisticVQA. PSNR is calculated as:

$$\mathrm{PSNR} = 10 \log_{10} \left(\frac{MAX_I^2}{\mathrm{MSE}} \right)$$

where $MAX_I=255$ for 8-bit video. SSIM is defined per Wang et al.:

$$\mathrm{SSIM}(x,y) = \frac{(2\mu_x\mu_y + C_1)(2\sigma_{xy}+C_2)}{(\mu_x^2+\mu_y^2 + C_1)(\sigma_x^2+\sigma_y^2 + C_2)}$$

To benchmark objective models against subjective scores, predictions $p_j$ are mapped to the subjective DMOS range using a four-parameter logistic function (VQEG):

$$f(p) = \beta_1 - \frac{\beta_2}{1+\exp\left(-\frac{p-\beta_3}{|\beta_4|}\right)} + \beta_2$$

The Pearson Linear Correlation Coefficient (PLCC) is then computed between $f(p_j)$ and $\mathrm{DMOS}_j$. Spearman’s Rank Correlation Coefficient (SRCC) is also reported. Benchmark results indicate that MinimalisticVQA achieves $\mathrm{PLCC}\approx0.596$ and $\mathrm{SRCC}\approx0.407$, revealing substantial headroom for models explicitly capturing temporal effects and the bitrate-frame-rate trade-off [2601.19776].

## 6. Significance and Context in HFR Video Assessment

HFR-LS is positioned among a limited set of publicly available HFR subjective datasets; it is unique in its systematic exploration of the joint effects of compression level and frame rate within a controlled bitrate ladder, designed for the live streaming context. Sequences are derived in part from major HFR benchmarks including BVI-HFR [2007.11634], UVG, and LIVE-YT-HFR [2007.11634], but HFR-LS distinguishes itself by its detailed, bitrate-aware encoding design and comprehensive MOS/DMOS labeling aligned with live streaming deployment strategies.

The dataset serves as a testbed for advancing VQA research in dynamic adaptive streaming, where efficient allocation between spatial and temporal fidelity is central. A plausible implication is that VQA models optimized with this dataset will better support perceptual-based bitrate adaptation algorithms, particularly for live streaming scenarios featuring camera motion, variable content complexity, and stringent real-time constraints.

## 7. Comparison to Related Resources and Application Scenarios

HFR-LS complements and extends prior HFR datasets such as LIVE-YT-HFR, which explores a broader range of frame rates (24–120 fps) and compression settings but is not specifically tailored to live streaming ladders or constrained bitrate regimes [2007.11634]. Datasets such as Need for Speed (NfS) target object tracking accuracy at extreme frame rates (240 fps) for vision tasks, emphasizing tracker performance rather than perceptual compression artifacts [1703.05884].

HFR-LS's explicit consideration of both encoding parameters and subjective quality under conditions representative of live platform streaming ladders makes it a pivotal resource for research in real-time adaptive streaming, perceptual optimization, and objective video quality prediction under bandwidth constraints.

Source: https://www.emergentmind.com/topics/high-frame-rate-live-streaming-hfr-ls-dataset