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.

翻译：逆色调映射（ITM）将标准动态范围（SDR）影像转换为高动态范围/宽色域（HDR/WCG）格式，用于媒体制作。该技术不仅应用于前端内容提供商对经典SDR素材的重制，还涉及用户端HDR显示器对实时SDR信号的适配。后者对效率要求更高，因此预计算查找表（LUT）成为主流方案。然而传统固定LUT缺乏适应性，故我们借鉴学界方法将其与人工智能相结合。考虑到更高位深的HDR/WCG格式需要比SDR更大的LUT，我们向传统ITM寻求效率与性能的平衡：采用三个小型LUT，每个LUT在暗区、中间区、亮区分别具有非均匀的精度分布。在此架构下，各LUT仅在其专属亮度区间内具有较低误差，因此我们引入贡献图融合三者的最优区域获得最终结果。在该贡献图的引导下，训练过程中三个LUT的元素（内容）也将被重新分配。我们通过消融实验验证方法有效性，并开展主观与客观实验展示其实用性。代码开源地址：https://github.com/AndreGuo/ITMLUT。