用轻量MLP提升HDR增益图编码，重建效果更好且内存固定
Gain-MLP: Improving HDR Gain Map Encoding via a Lightweight MLP
- 用轻量MLP基于SDR图像信息编码增益图
- 相比现有方法，HDR重建质量显著提升
- 仅需10KB内存，适配不同图像尺寸
虽然网络和社交媒体上大多数图片以标准动态范围（SDR）编码，但许多显示设备已支持高动态范围（HDR）内容。现代相机虽能捕捉HDR图像，却常转为SDR以兼容现有工作流程和旧显示设备。为同时支持SDR与HDR，新编码格式通过在SDR图像中嵌入增益图来实现。该增益图应用于SDR图像即可恢复HDR版本。然而，传统增益图常被下采样并使用JPEG、HEIC等标准压缩，导致不良伪影。本文提出使用轻量多层感知机（MLP）网络编码增益图，以SDR图像信息为输入进行优化，在HDR重建方面表现更优。该方法具有固定10 KB内存占用，无需针对不同图像尺寸或编码参数调整。我们在多种MLP-based HDR嵌入策略上进行了广泛实验，结果表明本方法优于当前最优方案。
原文摘要 · Abstract (English)
While most images shared on the web and social media platforms are encoded in standard dynamic range (SDR), many displays now can accommodate high dynamic range (HDR) content. Additionally, modern cameras can capture images in an HDR format but convert them to SDR to ensure maximum compatibility with existing workflows and legacy displays. To support both SDR and HDR, new encoding formats are emerging that store additional metadata in SDR images in the form of a gain map. When applied to the SDR image, the gain map recovers the HDR version of the image as needed. These gain maps, however, are typically down-sampled and encoded using standard image compression, such as JPEG and HEIC, which can result in unwanted artifacts. In this paper, we propose to use a lightweight multi-layer perceptron (MLP) network to encode the gain map. The MLP is optimized using the SDR image information as input and provides superior performance in terms of HDR reconstruction. Moreover, the MLP-based approach uses a fixed memory footprint (10 KB) and requires no additional adjustments to accommodate different image sizes or encoding parameters. We conduct extensive experiments on various MLP based HDR embedding strategies and demonstrate that our approach outperforms the current state-of-the-art.
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