# Gain-MLP: Improving HDR Gain Map Encoding via a Lightweight MLP

arXiv: https://arxiv.org/abs/2503.11883 · HTML: https://mlsanity.com/paper/2503.11883/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2025.

## Built on by (most cited first, 4 of 4)

- [GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction](https://mlsanity.com/paper/2512.16357/) (2025)
- [Color Pass-Through via Camera-Display Coupling](https://mlsanity.com/paper/2607.12746/) (2026)
- [Bio-SFT: Asymmetric Cortical Guidance and Retinal Adaptation for Robust HDR Reconstruction](https://mlsanity.com/paper/2607.17456/) (2026)
- [Dual-Output Multi-Exposure HDR Reconstruction via SDR Fusion and Gain Map Inverse Tone Mapping](https://mlsanity.com/paper/2608.05626/) (2026)

## Abstract (arXiv)

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.