mlsanityData to 14 Oct 2026

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

arXiv 2309.17160 ↗First posted 29 Sep 2023cs.MM cs.CVcited by 0

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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.

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.

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29 of 83 references.

References · 29

  1. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  2. 2020Zero-Reference Deep Curve Estimation for Low-Light Image Enhancementcited by 542
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  8. 2022AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancementcited by 70
  9. 2021Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tablescited by 65
  10. 2018Distort-and-Recover: Color Enhancement using Deep Reinforcement Learningcited by 52
  11. 2022SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancementcited by 50
  12. 2023HDR-VDP-3: A multi-metric for predicting image differences, quality and contrast distortions in high dynamic range and regular contentcited by 41
17 more
  1. 2019Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applicationscited by 39
  2. 20224D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancementcited by 34
  3. 2019JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Videocited by 31
  4. 2021A New Journey from SDRTV to HDRTVcited by 30
  5. 2023MEFLUT: Unsupervised 1D Lookup Tables for Multi-exposure Image Fusioncited by 22
  6. 2020BBAND Index: A No-Reference Banding Artifact Predictorcited by 19
  7. 2022SDRTV-to-HDRTV via Hierarchical Dynamic Context Feature Mappingcited by 13
  8. 2021How to cheat with metrics in single-image HDR reconstructioncited by 11
  9. 2023Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Modelscited by 11
  10. 2023NLUT: Neural-based 3D Lookup Tables for Video Photorealistic Style Transfercited by 10
  11. 2022Neural Color Operators for Sequential Image Retouchingcited by 8
  12. 2022Distilling Style from Image Pairs for Global Forward and Inverse Tone Mappingcited by 5
  13. 2022SDRTV-to-HDRTV Conversion via Spatial-Temporal Feature Fusioncited by 4
  14. 2022Towards Real World HDRTV Reconstruction: A Data Synthesis-based Approachcited by 4
  15. 2023Towards Efficient SDRTV-to-HDRTV by Learning from Image Formationcited by 4
  16. 2022Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New Benchmarkcited by 3
  17. 2023Dual Inverse Degradation Network for Real-World SDRTV-to-HDRTV Conversioncited by 3

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