# Redistributing the Precision and Content in 3D-LUT-based Inverse Tone-mapping for HDR/WCG Display

arXiv: https://arxiv.org/abs/2309.17160 · HTML: https://mlsanity.com/paper/2309.17160/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 83 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 29 of 29)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [Deep Bilateral Learning for Real-Time Image Enhancement](https://mlsanity.com/paper/1707.02880/) (2017)
- [HDR image reconstruction from a single exposure using deep CNNs](https://mlsanity.com/paper/1710.07480/) (2017)
- [Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time](https://mlsanity.com/paper/2009.14468/) (2020)
- [DeepLPF: Deep Local Parametric Filters for Image Enhancement](https://mlsanity.com/paper/2003.13985/) (2020)
- [Conditional Sequential Modulation for Efficient Global Image Retouching](https://mlsanity.com/paper/2009.10390/) (2020)
- [AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement](https://mlsanity.com/paper/2204.13983/) (2022)
- [Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables](https://mlsanity.com/paper/2108.08697/) (2021)
- [Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning](https://mlsanity.com/paper/1804.04450/) (2018)
- [SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement](https://mlsanity.com/paper/2207.08351/) (2022)
- [HDR-VDP-3: A multi-metric for predicting image differences, quality and contrast distortions in high dynamic range and regular content](https://mlsanity.com/paper/2304.13625/) (2023)
- [Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications](https://mlsanity.com/paper/1904.11176/) (2019)
- [4D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancement](https://mlsanity.com/paper/2209.01749/) (2022)
- [JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Video](https://mlsanity.com/paper/1909.04391/) (2019)
- [A New Journey from SDRTV to HDRTV](https://mlsanity.com/paper/2108.07978/) (2021)
- [MEFLUT: Unsupervised 1D Lookup Tables for Multi-exposure Image Fusion](https://mlsanity.com/paper/2309.11847/) (2023)
- [BBAND Index: A No-Reference Banding Artifact Predictor](https://mlsanity.com/paper/2002.11891/) (2020)
- [SDRTV-to-HDRTV via Hierarchical Dynamic Context Feature Mapping](https://mlsanity.com/paper/2207.00319/) (2022)
- [How to cheat with metrics in single-image HDR reconstruction](https://mlsanity.com/paper/2108.08713/) (2021)
- [Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models](https://mlsanity.com/paper/2303.13031/) (2023)
- [NLUT: Neural-based 3D Lookup Tables for Video Photorealistic Style Transfer](https://mlsanity.com/paper/2303.09170/) (2023)
- [Neural Color Operators for Sequential Image Retouching](https://mlsanity.com/paper/2207.08080/) (2022)
- [Distilling Style from Image Pairs for Global Forward and Inverse Tone Mapping](https://mlsanity.com/paper/2209.15165/) (2022)
- [Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation](https://mlsanity.com/paper/2309.04084/) (2023)
- [SDRTV-to-HDRTV Conversion via Spatial-Temporal Feature Fusion](https://mlsanity.com/paper/2211.02297/) (2022)
- [Towards Real World HDRTV Reconstruction: A Data Synthesis-based Approach](https://mlsanity.com/paper/2211.03058/) (2022)
- [Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New Benchmark](https://mlsanity.com/paper/2207.03367/) (2022)
- [Dual Inverse Degradation Network for Real-World SDRTV-to-HDRTV Conversion](https://mlsanity.com/paper/2307.03394/) (2023)

## Abstract (arXiv)

ITM(inverse tone-mapping) converts SDR (standard dynamic range) footage to HDR/WCG (high dynamic range /wide color gamut) for media production. It happens not only when remastering legacy SDR footage in front-end content provider, but also adapting on-theair SDR service on user-end HDR display. The latter requires more efficiency, thus the pre-calculated LUT (look-up table) has become a popular solution. Yet, conventional fixed LUT lacks adaptability, so we learn from research community and combine it with AI. Meanwhile, higher-bit-depth HDR/WCG requires larger LUT than SDR, so we consult traditional ITM for an efficiency-performance trade-off: We use 3 smaller LUTs, each has a non-uniform packing (precision) respectively denser in dark, middle and bright luma range. In this case, their results will have less error only in their own range, so we use a contribution map to combine their best parts to final result. With the guidance of this map, the elements (content) of 3 LUTs will also be redistributed during training. We conduct ablation studies to verify method's effectiveness, and subjective and objective experiments to show its practicability. Code is available at: https://github.com/AndreGuo/ITMLUT.