Empowering Resampling Operation for Ultra-High-Definition Image Enhancement with Model-Aware Guidance

Image enhancement algorithms have made remarkable advancements in recent years, but directly applying them to Ultra-high-definition (UHD) images presents intractable computational overheads. Therefore, previous straightforward solutions employ resampling techniques to reduce the resolution by adopting a "Downsampling-Enhancement-Upsampling" processing paradigm. However, this paradigm disentangles the resampling operators and inner enhancement algorithms, which results in the loss of information that is favored by the model, further leading to sub-optimal outcomes. In this paper, we propose a novel method of Learning Model-Aware Resampling (LMAR), which learns to customize resampling by extracting model-aware information from the UHD input image, under the guidance of model knowledge. Specifically, our method consists of two core designs, namely compensatory kernel estimation and steganographic resampling. At the first stage, we dynamically predict compensatory kernels tailored to the specific input and resampling scales. At the second stage, the image-wise compensatory information is derived with the compensatory kernels and embedded into the rescaled input images. This promotes the representation of the newly derived downscaled inputs to be more consistent with the full-resolution UHD inputs, as perceived by the model. Our LMAR enables model-aware and model-favored resampling while maintaining compatibility with existing resampling operators. Extensive experiments on multiple UHD image enhancement datasets and different backbones have shown consistent performance gains after correlating resizer and enhancer, e.g., up to 1.2dB PSNR gain for 1.8 times resampling scale on UHD-LOL4K. The code is available at https://github.com/YPatrickW/LMAR.