Different from color correction and transfer, color grading involves adjusting colors for artistic or storytelling purposes in a video, which is used to establish a specific look or mood. However, due to the complexity of the process and the need for specialized editing skills, video color grading remains primarily the domain of professional colorists. In this paper, we present a reference-based video color grading framework. Our key idea is explicitly generating a look-up table (LUT) for color attribute alignment between reference scenes and input video via a diffusion model. As a training objective, we enforce that high-level features of the reference scenes like look, mood, and emotion should be similar to that of the input video. Our LUT-based approach allows for color grading without any loss of structural details in the whole video frames as well as achieving fast inference. We further build a pipeline to incorporate a user-preference via text prompts for low-level feature enhancement such as contrast and brightness, etc. Experimental results, including extensive user studies, demonstrate the effectiveness of our approach for video color grading. Codes are publicly available at https://github.com/seunghyuns98/VideoColorGrading.

Since the widespread availability of cameras, black-and-white (BW)photography has been a popular choice for artistic and aesthetic expression. It highlights the main subject in varying tones of gray, creating various effects such as drama and contrast. However, producing BW photog-raphy often demands high-end cameras or photographic editing from experts. Even the experts prefer different styles depending on the subject or even the same subject when taking grayscale photos or converting color images to BW. It is thus questionable which approach is better. To imitate the artistic values of decolorized images, this paper introduces a deep metric learning framework with a novel subject-style specified proxy and a large-scale BW dataset. Our proxy-based decolorization utilizes a hierar-chical proxy-based loss and a hierarchical bilateral grid network to mimic the experts' retouching scheme. The proxy-based loss captures both expert-discriminative and class-sharing characteristics, while the hierarchical bilateral grid network enables imitating spatially-variant retouching by considering both global and local scene contexts. Our dataset, including color and BW images edited by three experts, demonstrates the scalability of our method, which can be further enhanced by constructing additional proxies from any set of BW photos like Internet downloaded figures. Our Experiments show that our framework successfully produce visually-pleasing BW images from color ones, as evaluated by user preference with respect to artistry and aesthetics. Code and dataset are publicly available at https://github.com/seunghyuns98IDecolorization.

Understanding the informative structures of scenes is essential for low-level vision tasks. Unfortunately, it is difficult to obtain a concrete visual definition of the informative structures because influences of visual features are task-specific. In this paper, we propose a single general neural network architecture for extracting task-specific structure guidance for scenes. To do this, we first analyze traditional spectral clustering methods, which computes a set of eigenvectors to model a segmented graph forming small compact structures on image domains. We then unfold the traditional graph-partitioning problem into a learnable network, named Scene Structure Guidance Network (SSGNet), to represent the task-specific informative structures. The SSGNet yields a set of coefficients of eigenvectors that produces explicit feature representations of image structures. In addition, our SSGNet is light-weight (56K parameters), and can be used as a plug-and-play module for off-the-shelf architectures. We optimize the SSGNet without any supervision by proposing two novel training losses that enforce task-specific scene structure generation during training. Our main contribution is to show that such a simple network can achieve state-of-the-art results for several low-level vision applications including joint upsampling and image denoising. We also demonstrate that our SSGNet generalizes well on unseen datasets, compared to existing methods which use structural embedding frameworks. Our source codes are available at https://github.com/jsshin98/SSGNet.