Multi-color holograms rely on simultaneous illumination from multiple light
sources. These multi-color holograms could utilize light sources better than
conventional single-color holograms and can improve the dynamic range of
holographic displays. In this letter, we introduce \projectname, the first
learned method for estimating the optimal light source powers required for
illuminating multi-color holograms. For this purpose, we establish the first
multi-color hologram dataset using synthetic images and their depth
information. We generate these synthetic images using a trending pipeline
combining generative, large language, and monocular depth estimation models.
Finally, we train our learned model using our dataset and experimentally
demonstrate that \projectname significantly decreases the number of steps
required to optimize multi-color holograms from $>1000$ to $70$ iteration steps
without compromising image quality.

本文介绍了 \projectname—— 第一种学习方法，用于估计照明多色全息图所需的最佳光源功率，并利用合成图像和深度信息建立了第一个多色全息图数据集。通过使用我们的数据集训练所学模型，实验表明 \projectname 可以在不损害图像质量的情况下将优化多色全息图所需的步骤数从 1000 个以上减少到 70 个。

AutoColor：用于多色全息图的学习型光功率控制

AutoColor: Learned Light Power Control for Multi-Color Holograms

Pre-trained convolutional neural networks (CNNs) are powerful off-the-shelf
feature generators and have been shown to perform very well on a variety of
tasks. Unfortunately, the generated features are high dimensional and expensive
to store: potentially hundreds of thousands of floats per example when
processing videos. Traditional entropy based lossless compression methods are
of little help as they do not yield desired level of compression, while general
purpose lossy compression methods based on energy compaction (e.g. PCA followed
by quantization and entropy coding) are sub-optimal, as they are not tuned to
task specific objective. We propose a learned method that jointly optimizes for
compressibility along with the task objective for learning the features. The
plug-in nature of our method makes it straight-forward to integrate with any
target objective and trade-off against compressibility. We present results on
multiple benchmarks and demonstrate that our method produces features that are
an order of magnitude more compressible, while having a regularization effect
that leads to a consistent improvement in accuracy.

为了解决传统基于信息熵的无损压缩方法在降维时效果不佳的问题，本文提出了一种基于学习的方法，可以同时优化特征的可压缩性和任务目标，从而生产一个更具压缩性的特征，同时提高其准确性。