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
Figures & tables
Figure 1: Overview of our long video color modernization framework. We first partition the input video into shots and select a representative keyframe from each shot. We then jointly modernize temporally adjacent keyframes. Finally, we estimate a shot-specific color mapping from each original–modernized keyframe pair and apply it to all frames in the corresponding shot.
Figure 2: Distribution of contemporary color preference rankings over 975 complete rankings. Each bar shows the proportions of Rank 1, Ranks 2–3, and Ranks 4–8, with the mean rank reported on the right.
Method
Edge-SSIM ↑
CDC ↓
SW-IGC ↓
DeQA ↑
Q-Align Q ↑
Q-Align A ↑
FPS ↑
Original
0.966
0.56
0.19
2.73
3.16
2.32
–
End-to-End Video Editing
CogVideoX-5B
0.439
0.77
–
2.32
2.55
1.92
–
Wan2.1-VACE-1.3B
0.535
1.37
–
1.92
1.96
1.63
–
Modular Color Propagation
Neural Preset
0.886
0.56
7.72
2.73
3.11
2.34
820
Table 1: Complementary quantitative comparison on 100 videos from the Condensed Movies Dataset. The unedited source video (Original) is shown in gray for reference and excluded from ranking. CDC is reported in units of 10−2 . SW-IGC is omitted for end-to-end methods since reliable source–output correspondence is unavailable. FPS denotes the propagation throughput on an NVIDIA H200 GPU (see Appendix B.4 ).
Figure 3: Qualitative comparison of video modernization methods on representative frames.
Strategy
Invocations
SW-IGC ↓
Independent
N
7.23
Independent + post-hoc smoothing
N
4.46 †
Packing (clustered)
⌈N/4⌉
5.87
Packing (consecutive)
⌈N/4⌉
5.90
Table 2: Ablation of keyframe modernization strategies. N denotes the number of shots in each video, and the packing strategies process up to four keyframes per model invocation. † Post-hoc smoothing directly regularizes the shot-specific mappings evaluated by SW-IGC.
Figure 4: Qualitative comparison of inter-shot consistency strategies for keyframe modernization. Independent modernization exhibits inter-shot color inconsistency, while post-hoc smoothing produces unnatural color distortions. The two joint strategies produce more coherent keyframe appearances across shots.
Color mapping operator
SWD ↓
Edge-SSIM ↑
Diagonal
0.821
0.884
Histogram
0.490
0.856
MKL
0.742
0.883
MKL + roll-off
0.748
0.885
Table 3: Ablation of shot-specific color mapping operators. Each operator is fitted and evaluated on the same source keyframe and its modernized counterpart.
Figure 5: Qualitative comparison of shot-specific color propagation operators. (A) Results obtained by applying each fitted mapping to its source keyframe. Top: Diagonal fails to reproduce the target grade, whereas Histogram introduces local color artifacts. Bottom: The per-channel mappings fail to reproduce the cross-channel target grade and produce strong color distortions, whereas MKL closely matches it. (B) Results after propagating the fitted mapping to a brighter frame: hard clipping saturates highlights, while roll-off preserves their tonal variation.
Appendix figures & tables9 assets
Supplementary material from the paper’s appendix.
Appendix
Configuration
Value
Keyframe Extraction
Shot detection
detector
TransNet V2
boundary threshold
0.1
Shot merging
boundary encoder
DINOv2-S
Appendix
Table 4: Pipeline configurations used for all experiments.
Stage
Device
Cost
Shot detection
CPU
∼ 24 s / min of video
Shot merging
GPU
∼ 0.9 s / shot
Keyframe selection
GPU
∼ 13 s / shot
Image-level modernization
GPU
∼ 14 s / grid
Color propagation
CPU
∼ 80 FPS
Appendix
Table 5: Per-stage processing cost, pooled over ten videos from our dataset.
Method
Mean Rank ↓
Rank-1 ↑
Top-3 ↑
Ours
2.313
37.9%
81.7%
CAP-VSTNet
3.015
17.6%
66.3%
Video Color Grading
3.559
12.9%
49.0%
Original
3.619
13.3%
45.1%
Neural Preset
4.028
11.0%
38.6%
SA-LUT
5.461
5.8%
12.8%
Appendix
Table 6: Human evaluation of modern color preference over 975 complete rankings (65 participants × 15 videos). Mean Rank uses all placements from 1 to 8; Rank-1 and Top-3 denote the percentage of rankings in the corresponding range.
Metric
Value
Videos
15
Methods per video
8
Unordered method pairs per video ( (28) )
28
2AFC queries per pair (forward + reversed order)
2
Total VLM queries
840
Complete swapped-order comparisons
420
Appendix
Table 7: Order-swap stability of the VLM-based two-alternative forced-choice (2AFC) color judge. For each unordered method pair, we query the judge twice: once in the original A/B order and once with the two candidates swapped. A comparison is consistent only when both queries select the same underlying method after mapping the A/B labels back to method identities.
Figure 6: Average pairwise absolute differences between the VLM scores of the eight evaluated methods across 15 videos.
Figure 7: Comparison of keyframe selection methods. Each row shows the keyframes selected by different methods for the same shot; failure cases are outlined in red.
Figure 8: Instructions presented to participants in the user study.
Figure 10: Additional qualitative comparisons between our method and the evaluated baselines on diverse frames from the evaluation set (Part 1).
Figure 11: Additional qualitative comparisons between our method and the evaluated baselines on diverse frames from the evaluation set (Part 2).