Botya/FH6-Temporal-Upscaling-Benchmark

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

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.

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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:

$$\mathrm{\Delta }x,\mathrm{\Delta }y=\arg \underset{(x,y)}{\max }{\mathcal{F}}^{-1}\left\{\frac{\mathcal{F}(I_{\text{TAA}})\cdot {\mathcal{F}}^{\ast }(I_{\text{FSR}})}{\mathrm{\mid }\mathcal{F}(I_{\text{TAA}})\cdot {\mathcal{F}}^{\ast }(I_{\text{FSR}})\mathrm{\mid }}\right\}\backslash Delta\; x,\; \backslash Delta\; y\; =\; \backslash arg\backslash max\_\{(x,y)\}\; \backslash mathcal\{F\}^\{-1\}\; \backslash left\backslash \{\; \backslash frac\{\backslash mathcal\{F\}(I\_\{\backslash text\{TAA\}\})\; \backslash cdot\; \backslash mathcal\{F\}^*(I\_\{\backslash text\{FSR\}\})\}\{\backslash vert\{\}\backslash mathcal\{F\}(I\_\{\backslash text\{TAA\}\})\; \backslash cdot\; \backslash mathcal\{F\}^*(I\_\{\backslash text\{FSR\}\})\backslash vert\{\}\}\; \backslash right\backslash \}$$

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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.

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Quickstart Usage

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

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")