用可解释参数实现视频调色，用户能精准控制效果。
NCST: Neural-based Color Style Transfer for Video Retouching
- 通过神经网络预测可解释的调色参数，提升过程透明度。
- 在关键帧上微调网络，生成精确的风格转换参数。
- 参数具明确含义，支持人工手动调优，适合内容创作者。
视频色彩风格迁移旨在利用参考风格图像改变原始视频的色彩风格。现有方法多采用神经网络，但存在过程不透明、用户难以精细调控等问题。为此，本文提出一种新方法：通过两张图像预测具体的色彩调整参数。首先训练神经网络学习对应的颜色调节参数；在实际应用中，使用视频的关键帧与目标风格图像对网络进行微调，生成精确的转换参数，进而应用于图像和视频的色彩风格迁移。实验表明，该方法在色彩风格迁移质量上优于现有技术。此外，每个参数均有明确、可解释的含义，使用户能够理解迁移过程，并根据需要进行手动微调。
原文摘要 · Abstract (English)
Video color style transfer aims to transform the color style of an original video by using a reference style image. Most existing methods employ neural networks, which come with challenges like opaque transfer processes and limited user control over the outcomes. Typically, users cannot fine-tune the resulting images or videos. To tackle this issue, we introduce a method that predicts specific parameters for color style transfer using two images. Initially, we train a neural network to learn the corresponding color adjustment parameters. When applying style transfer to a video, we fine-tune the network with key frames from the video and the chosen style image, generating precise transformation parameters. These are then applied to convert the color style of both images and videos. Our experimental results demonstrate that our algorithm surpasses current methods in color style transfer quality. Moreover, each parameter in our method has a specific, interpretable meaning, enabling users to understand the color style transfer process and allowing them to perform manual fine-tuning if desired.
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