---
license: cc-by-nc-4.0
task_categories:
  - image-to-image
  - video-classification
tags:
  - super-resolution
  - temporal-upscaling
  - amd-fsr
  - taa
  - computer-vision
  - video-restoration
  - benchmark
pretty_name: Forza Horizon Temporal Upscaling Benchmark (TAA vs. FSR 4.1.1)
size_categories:
  - 1K<n<10K
---

<div align="center">

# Forza Horizon Temporal Upscaling Benchmark (TAA vs. AMD FSR 4.1.1)

[![Dataset Version](https://img.shields.io/badge/Release-v1.0%20Benchmark-blue.svg)](https://huggingface.co/datasets/Botya/FH6-Temporal-Upscaling-Benchmark)
[![Samples](https://img.shields.io/badge/Samples-1%2C041%20Pairs-success.svg)](#dataset-structure--schema)
[![Lossless](https://img.shields.io/badge/Format-Lossless%20RGB24%20(512x512)-orange.svg)](#capture--synchronization-methodology)
[![Total Size](https://img.shields.io/badge/Size-701%20MB-purple.svg)](#dataset-structure--schema)
[![License](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)

</div>

## Dataset Summary
This dataset provides **1,041 pixel-aligned, lossless 512x512 RGB paired image tensors** (2,082 total patches) curated to evaluate temporal upscaling artifacts—focusing on disocclusion ghosting, high-frequency specular shimmering, and motion smearing—comparing **AMD FidelityFX Super Resolution (FSR 4.1.1)** against **Native Temporal Anti-Aliasing (TAA)** ground truth.

Each sample includes horizontal (`x_shift_pixels`) and vertical (`y_shift_pixels`) sub-pixel temporal drift metrics computed via 2D Fourier Phase Correlation, validating strict spatial alignment without applying synthetic affine warping or lossy interpolation to the raw pixel data.

---

## Capture & Synchronization Methodology

### Benchmark Hardware & Rendering Pipeline
* **Hardware Architecture:** AMD Ryzen AI 9 / Radeon 890M integrated graphics architecture.
* **Display Output:** 1920x1080 native rasterization; engine V-Sync disabled to prevent buffered frame-queue latency.
* **Frame Pacing Lock:** Hardware-level frametime limiter enforced via RivaTuner Statistics Server (RTSS) locked to 30.00 FPS (strict 33.33 ms frame delivery, zero frame drops).
* **Capture Format:** OBS Studio AMF hardware recording to Constant Frame Rate (CFR) Matroska (`.mkv`) containers utilizing 4:4:4 planar color sampling (`I444`), eliminating chroma subsampling artifacts (4:2:0).
* **Patch Extraction:** Lossless decoding to 24-bit RGB tensors via OpenCV. Central 512x512 regions were cropped to isolate vehicle motion and reflection vectors while excluding all 2D HUD text, UI alerts, and physics debris.

### Sub-Pixel Verification Telemetry
Temporal synchronization was achieved through temporal Mean Squared Error (MSE) minimization over a HUD-isolated search window. Exact sub-pixel spatial displacement was logged per frame using 2D Fourier Phase Correlation:

$$\Delta x, \Delta y = \arg\max_{(x,y)} \mathcal{F}^{-1} \left\{ \frac{\mathcal{F}(I_{\text{TAA}}) \cdot \mathcal{F}^*(I_{\text{FSR}})}{\vert{}\mathcal{F}(I_{\text{TAA}}) \cdot \mathcal{F}^*(I_{\text{FSR}})\vert{}} \right\}$$

---

## Dataset Structure & Schema

The dataset contains a single `train` split consisting of 1,041 verified image pairs:

| Feature Name | Type | Description |
| :--- | :--- | :--- |
| `clip_id` | `string` | Anchor identifier of the source video window. |
| `frame_index` | `int32` | Sequential frame index within the extracted window (`1`–`150`). |
| `frame_filename` | `string` | Source frame filename (`frame_00001.png` to `frame_00150.png`). |
| `taa_ground_truth` | `Image` | 512x512 lossless PNG patch rendered with Native TAA. |
| `fsr_upscaled` | `Image` | 512x512 lossless PNG patch rendered with AMD FSR 4.1.1. |
| `x_shift_pixels` | `float32` | Horizontal sub-pixel displacement calculated via Phase Correlation. |
| `y_shift_pixels` | `float32` | Vertical sub-pixel displacement calculated via Phase Correlation. |

---

## Quickstart Usage

Stream or download the dataset directly using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

# Stream the dataset directly into Python
dataset = load_dataset("Botya/FH6-Temporal-Upscaling-Benchmark", split="train")

# Retrieve and inspect the first paired sample
sample = dataset[0]
taa_patch = sample["taa_ground_truth"]
fsr_patch = sample["fsr_upscaled"]

print(f"Loaded {sample['clip_id']} [{sample['frame_filename']}]")
print(f"Spatial Drift: dX={sample['x_shift_pixels']:.4f}px, dY={sample['y_shift_pixels']:.4f}px")