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This paper introduces LUT-Opt, a two-stage framework for adaptive per-frame rendering parameter optimization that balances rendering time and image quality. In the offline stage, XGBoost regressors are trained to predict rendering time and image quality, and then distilled into compact lookup tables (LUTs). At runtime, these LUTs are queried to enable adaptive parameter selection with minimal overhead, achieving significant speedups in subsurface scattering and ambient occlusion rendering within Unreal Engine 5.
Achieve up to 70% faster rendering by distilling XGBoost models into lookup tables that adapt rendering parameters on a per-frame basis with sub-millisecond latency.
Achieving a desirable balance between rendering quality and real-time performance is a long-standing challenge in modern game and rendering engines, particularly on resource-constrained mobile devices such as laptops, tablets, and smartphones. Existing approaches to automatic rendering parameter optimization either depend on exhaustive per-scene pre-computation that spans several days, suffer from the prohibitive inference overhead of neural networks that prevents per-frame adaptation, or lack generalizability across heterogeneous hardware and diverse scenes. In this paper, we propose \textbf{LUT-Opt}, a lightweight, general-purpose framework for adaptive per-frame rendering parameter optimization. Our method decomposes the joint optimization of rendering time and image quality into a tractable two-stage pipeline. In the offline stage, we train a pair of XGBoost regressors to predict rendering time and image quality from rendering parameters, hardware state, and scene complexity descriptors. The trained ensemble models are then distilled into compact lookup tables (LUTs) through systematic discretization and a two-phase linear search that first constrains rendering time and subsequently maximizes structural similarity (SSIM). During runtime, the pre-computed LUT is queried every frame in sub-millisecond time, enabling truly adaptive parameter selection with negligible computational overhead. We validate LUT-Opt on two representative rendering techniques -- subsurface scattering (SSS) and hybrid-pipeline ambient occlusion (AO) -- implemented within Unreal Engine 5. Extensive experiments across multiple scenes and GPU configurations demonstrate that LUT-Opt reduces subsurface scattering rendering time by approximately 40\% and ambient occlusion rendering time by roughly 70\%, while incurring only about 2\% increase in image quality error, with per-frame inference latency below 0.1\ ms.
