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 photography often demands high-end cameras or photographic editing from experts. Even the experts have their own preferred styles, and may also favor different styles depending on the subject when taking gray-scale 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 hierarchical 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 produces visually-pleasing BW images from color ones, as evaluated by user preference with respect to artistry and aesthetics.