Scalable method brings legacy video colors up to date
From Legacy to Contemporary: Scalable Color Modernization for Long Video
Computer Vision and Pattern Recognition
Summary
Old videos often have faded or unnatural colors that make them look outdated. The authors created a way to update the colors in these long videos so they look more modern, while keeping the scenes consistent and recognizable. Their method doesn’t need special training or paired examples, making it easier to use on long videos. They tested their approach on videos from the last 50 years and found it improved how modern the videos look without hurting quality or stability.
What this means in practice
- •For post-production teams: Improve the color quality of long legacy videos efficiently without manual expertise, enabling consistent modern looks across multi-shot footage.
- •For video streaming services: Automatically update and modernize colors of archival video catalogs to enhance viewer experience on modern displays.
Authors
Dongyun Kim, Jiwoo Choi, Jaeseong Lee, Seon Joo Kim
Abstract
Bringing legacy video back to contemporary screens does not necessarily make it look contemporary. Earlier imaging pipelines often leave color casts, muted chroma, weak con- trast, and tonal imbalance, while correcting these character- istics still relies heavily on expert colorists and scales poorly to long, multi-shot videos. We formulate long video color modernization, a task that updates the chromatic and tonal appearance of legacy color footage while preserving scene content and coherence across frames and shots. We introduce a scalable, training-free framework that jointly modernizes grids of temporally adjacent keyframes using a pretrained text-guided image editing model, producing coherent con- temporary references without paired supervision. Compact shot-specific color transforms then propagate these adjust- ments throughout each shot, preserving spatial structure and temporal stability. We conduct a multidimensional evaluation on diverse legacy videos spanning five decades. We combine a human evaluation of perceived modernness with quantita- tive measures of content fidelity, temporal stability, percep- tual quality, and efficiency, and introduce Source-Weighted Inter-shot Grade Consistency (SW-IGC) to assess grade co- herence across shots. On our evaluation set, our frame- work achieves the highest perceived modernness while re- maining competitive across complementary measures, open- ing a path toward the systematic study of long video color modernization. Code is available at https://github.com/jiwoomood/ Video_Color_Modernization.git