标题： NCST：基于神经网络的视频润色颜色风格迁移 显示英文标题

标题： NCST: Neural-based Color Style Transfer for Video Retouching

摘要： 视频色彩风格迁移旨在通过使用参考风格图像来转换原始视频的色彩风格。 大多数现有方法采用神经网络，这带来了诸如迁移过程不透明和用户对结果控制有限等挑战。 通常，用户无法对生成的图像或视频进行微调。 为了解决这个问题，我们引入了一种方法，该方法使用两张图像预测色彩风格迁移的具体参数。 首先，我们训练一个神经网络以学习相应的色彩调整参数。 在对视频进行风格迁移时，我们使用视频中的关键帧和选定的风格图像对网络进行微调，生成精确的变换参数。 然后将这些参数应用于转换图像和视频的色彩风格。 我们的实验结果表明，我们的算法在色彩风格迁移质量方面优于当前方法。 此外，我们方法中的每个参数都有特定且可解释的含义，使用户能够理解色彩风格迁移过程，并在需要时进行手动微调。 显示英文摘要

摘要： 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.