# SDRTV-to-HDRTV via Hierarchical Dynamic Context Feature Mapping

arXiv: https://arxiv.org/abs/2207.00319 · HTML: https://mlsanity.com/paper/2207.00319/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2022.

## Built on by (most cited first, 13 of 13)

- [GlowGAN: Unsupervised Learning of HDR Images from LDR Images in the Wild](https://mlsanity.com/paper/2211.12352/) (2022)
- [Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models](https://mlsanity.com/paper/2303.13031/) (2023)
- [Beyond Alignment: Blind Video Face Restoration via Parsing-Guided Temporal-Coherent Transformer](https://mlsanity.com/paper/2404.13640/) (2024)
- [AIM 2025 challenge on Inverse Tone Mapping Report: Methods and Results](https://mlsanity.com/paper/2508.13479/) (2025)
- [Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation](https://mlsanity.com/paper/2309.04084/) (2023)
- [GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction](https://mlsanity.com/paper/2403.17837/) (2024)
- [Beyond Feature Mapping GAP: Integrating Real HDRTV Priors for Superior SDRTV-to-HDRTV Conversion](https://mlsanity.com/paper/2411.10775/) (2024)
- [Mitigating Knowledge Conflicts in Language Model-Driven Question Answering](https://mlsanity.com/paper/2411.11344/) (2024)
- [Redistributing the Precision and Content in 3D-LUT-based Inverse Tone-mapping for HDR/WCG Display](https://mlsanity.com/paper/2309.17160/) (2023)
- [A Cycle Ride to HDR: Semantics Aware Self-Supervised Framework for Unpaired LDR-to-HDR Image Reconstruction](https://mlsanity.com/paper/2410.15068/) (2024)
- [RealRep: Generalized SDR-to-HDR Conversion via Attribute-Disentangled Representation Learning](https://mlsanity.com/paper/2505.07322/) (2025)
- [PhysHDR: When Lighting Meets Materials and Scene Geometry in HDR Reconstruction](https://mlsanity.com/paper/2509.16869/) (2025)
- [LumaFlux: Lifting 8-Bit Worlds to HDR Reality with Physically-Guided Diffusion Transformers](https://mlsanity.com/paper/2604.02787/) (2026)

## Abstract (arXiv)

In this work, we address the task of SDR videos to HDR videos(SDRTV-to-HDRTV). Previous approaches use global feature modulation for SDRTV-to-HDRTV. Feature modulation scales and shifts the features in the original feature space, which has limited mapping capability. In addition, the global image mapping cannot restore detail in HDR frames due to the luminance differences in different regions of SDR frames. To resolve the appeal, we propose a two-stage solution. The first stage is a hierarchical Dynamic Context feature mapping (HDCFM) model. HDCFM learns the SDR frame to HDR frame mapping function via hierarchical feature modulation (HME and HM ) module and a dynamic context feature transformation (DCT) module. The HME estimates the feature modulation vector, HM is capable of hierarchical feature modulation, consisting of global feature modulation in series with local feature modulation, and is capable of adaptive mapping of local image features. The DCT module constructs a feature transformation module in conjunction with the context, which is capable of adaptively generating a feature transformation matrix for feature mapping. Compared with simple feature scaling and shifting, the DCT module can map features into a new feature space and thus has a more excellent feature mapping capability. In the second stage, we introduce a patch discriminator-based context generation model PDCG to obtain subjective quality enhancement of over-exposed regions. PDCG can solve the problem that the model is challenging to train due to the proportion of overexposed regions of the image. The proposed method can achieve state-of-the-art objective and subjective quality results. Specifically, HDCFM achieves a PSNR gain of 0.81 dB at a parameter of about 100K. The number of parameters is 1/14th of the previous state-of-the-art methods. The test code will be released soon.