用轻量AI模型实现游戏画质超分，省电又高效。
LCS: An AI-based Low-Complexity Scaler for Power-Efficient Super-Resolution of Game Content
- 基于高效超分模型设计轻量级缩放器，可部署在低功耗NPU上。
- 在五项指标中表现优于AMD硬件方案，视觉质量更优。
- 适合资源受限设备使用，如移动平台或嵌入式系统。
现代游戏内容渲染复杂度上升导致GPU负载显著增加。本文提出一种受先进高效超分辨率（ESR）模型启发的低复杂度缩放器（LCS），可将GPU工作负载卸载至低功耗设备（如神经推理单元NPU）。LCS在原生低/高分辨率游戏图像对（GameIR）上训练，采用对抗训练增强感知重要细节重建，并通过重参数化与量化技术降低模型复杂度与体积。在五项指标上的对比评估显示，该方法在与公开的AMD硬件级边缘自适应缩放功能（EASF）及FidelityFX Super Resolution 1（FSR1）比较中，实现了更优的感知质量，证明了ESR模型在资源受限设备上进行画面超分的潜力。
原文摘要 · Abstract (English)
The increasing complexity of content rendering in modern games has led to a problematic growth in the workload of the GPU. In this paper, we propose an AI-based low-complexity scaler (LCS) inspired by state-of-the-art efficient super-resolution (ESR) models which could offload the workload on the GPU to a low-power device such as a neural processing unit (NPU). The LCS is trained on GameIR image pairs natively rendered at low and high resolution. We utilize adversarial training to encourage reconstruction of perceptually important details, and apply reparameterization and quantization techniques to reduce model complexity and size. In our comparative analysis we evaluate the LCS alongside the publicly available AMD hardware-based Edge Adaptive Scaling Function (EASF) and AMD FidelityFX Super Resolution 1 (FSR1) on five different metrics, and find that the LCS achieves better perceptual quality, demonstrating the potential of ESR models for upscaling on resource-constrained devices.
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