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

Abstract

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.

Accepted in CVMP2023 (the 20th ACM SIGGRAPH European Conference on Visual Media Production)

References in corpus (5)

• Deep Bilateral Learning for Real-Time Image Enhancement
• HDR image reconstruction from a single exposure using deep CNNs
• Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time
• Neural Color Operators for Sequential Image Retouching
• Distilling Style from Image Pairs for Global Forward and Inverse Tone Mapping