HAVC
May 28, 2026 · View on GitHub
This document contains the full per-image CIEDE2000 results of the colorization models supported by HAVC. For an introduction to the compared models and a high-level summary of the findings, see the main README.
Comparison Methodology
To compare the models I decided to use a metric being able to consider the perceptual non-uniformities in the evaluation of color difference between images. These non-uniformities are important because the human eye is more sensitive to certain colors than others. Over time, The International Commission on Illumination (CIE) has proposed increasingly advanced measurement models to measure the color distance taking into account the human color perception, that they called dE. One of the most advanced is the CIEDE2000 method, that I decided to use as color similarity metric to compare the models. The final results are shown in the tables below (test image can be seen by clicking on the test number).
The calculation of dE with the CIEDE2000 method was obtained by leveraging on the computational code available in ColorMine.
Test Set #1
The compared models in this test set are:
- D+D: DeOldify (with model Video & render_factor = 24) + DDColor (with model Artistic and render_factor = 24)
- DD: DDColor (with model Artistic and render_factor = 24, equivalent to input_size = 384)
- DS: DeOldify (with model Stable & render_factor = 24)
- DV: DeOldify (with model Video & render_factor = 24)
- T241: ColTran + TensorFlow 2.4.1 model as shown in Habr
| Test # | D+D | DD | DS | DV | T241 |
|---|---|---|---|---|---|
| 01 | 10.7 | 8.7 | 8.8 | 12.7 | 15.7 |
| 02 | 11.8 | 11.7 | 12.7 | 12.7 | 15.9 |
| 03 | 5.5 | 3.8 | 5.6 | 7.6 | 9.9 |
| 04 | 6.2 | 8.5 | 4.6 | 5.3 | 9.0 |
| 05 | 6.6 | 8.4 | 8.8 | 8.6 | 12.5 |
| 06 | 10.2 | 9.9 | 10.6 | 11.2 | 16.4 |
| 07 | 6.5 | 6.7 | 6.8 | 7.7 | 10.2 |
| 08 | 6.7 | 6.4 | 7.5 | 8.3 | 9.9 |
| 09 | 11.7 | 11.7 | 15.2 | 13.8 | 16.5 |
| 10 | 7.8 | 8.0 | 9.1 | 8.4 | 9.5 |
| 11 | 7.5 | 8.0 | 8.0 | 7.8 | 14.8 |
| 12 | 7.7 | 7.6 | 8.6 | 7.8 | 13.7 |
| 13 | 11.8 | 11.9 | 14.2 | 13.7 | 16.8 |
| 14 | 5.3 | 5.2 | 4.4 | 5.3 | 7.2 |
| 15 | 8.2 | 7.3 | 10.7 | 10.6 | 15.7 |
| 16 | 12.0 | 12.3 | 9.8 | 12.7 | 19.7 |
| 17 | 11.1 | 10.2 | 11.6 | 12.4 | 16.7 |
| 18 | 6.7 | 9.3 | 7.2 | 8.6 | 13.1 |
| 19 | 3.7 | 4.4 | 4.7 | 3.9 | 4.6 |
| 20 | 8.7 | 10.1 | 6.9 | 9.2 | 11.0 |
| 21 | 6.9 | 6.9 | 8.1 | 8.4 | 10.4 |
| 22 | 11.5 | 11.8 | 13.3 | 12.2 | 12.7 |
| 23 | 5.6 | 7.1 | 11.4 | 8.8 | 11. |
| Avg(dE) | 8.3 | 8.5 | 9.1 | 9.5 | 12.7 |
As it is possible to see the model that performed better is the D+D model (which I called HAVC ddelodify because is using both DeOldify and DDColor). This model was the best model in 10 tests out of 23. Also the DD model performed well but there were situations where the DD model provided quite bad colorized images like in Test #23 and the combination with the DeOldify allowed to significantly improve the final image. In effect the average distance of DD was 8.5 while for DV was 9.5, given that the 2 models were weighted at 50%, if the images were positively correlated a value 9.0 would have been expected, instead the average distance measured for D+D was 8.3, this implies that the 2 models were able to compensate each other. Conversely, the T241 was the model that performed worse with the greatest average difference in colors. Finally, the quality of DeOldify models was similar, being DS slightly better than DV (as expected).
Tests Set #2
Given the goodness of CIEDE2000 method to provide a reliable estimate of human color perception, I decided to provide an additional set of tests including some of the cases not considered previously.
The models added are:
- DA: DeOldify (with model Artistic & render_factor = 30)
- DDs: DDColor (with model ModelScope and input_size = 384)
- DS+DD: DeOldify (with model Stable & render_factor = 30) + DDColor (with model Artistic and render_factor = 24)
- DA+DDs: DeOldify (with model Artistic & render_factor = 30) + DDColor (with model ModelScope and render_factor = 24)
- DA+DD: DeOldify (with model Artistic & render_factor = 30) + DDColor (with model Artistic and render_factor = 24)
The results of this additional tests set are shown in the table below (test image can be seen by clicking on the test number)
| Test # | DS+DD | DA+DDs | DA+DD | DDs | DA |
|---|---|---|---|---|---|
| 01 | 7.7 | 7.5 | 8.2 | 8.2 | 8.6 |
| 02 | 11.8 | 11.4 | 11.9 | 11.6 | 13.2 |
| 03 | 4.5 | 4.2 | 3.9 | 4.5 | 4.2 |
| 04 | 5.9 | 5.1 | 6.0 | 6.6 | 5.9 |
| 05 | 6.4 | 6.5 | 6.7 | 9.5 | 9.0 |
| 06 | 10.0 | 10.0 | 10.3 | 9.5 | 11.4 |
| 07 | 6.1 | 7.3 | 6.6 | 8.1 | 8.0 |
| 08 | 6.2 | 8.1 | 7.3 | 8.1 | 9.4 |
| 09 | 12.7 | 11.3 | 11.5 | 12.5 | 13.3 |
| 10 | 8.1 | 7.7 | 8.0 | 7.1 | 9.0 |
| 11 | 7.2 | 7.3 | 7.4 | 8.6 | 7.9 |
| 12 | 8.0 | 7.1 | 8.0 | 6.5 | 9.3 |
| 13 | 12.0 | 11.7 | 12.0 | 11.8 | 13.8 |
| 14 | 4.5 | 4.6 | 4.8 | 5.8 | 4.8 |
| 15 | 8.3 | 8.1 | 8.9 | 8.2 | 12.2 |
| 16 | 10.6 | 10.5 | 10.7 | 12.5 | 9.9 |
| 17 | 10.8 | 12.1 | 11.4 | 12.3 | 13.5 |
| 18 | 6.7 | 7.1 | 6.1 | 11.1 | 7.2 |
| 19 | 3.5 | 4.6 | 4.5 | 5.1 | 7.1 |
| 20 | 8.0 | 8.1 | 8.2 | 9.3 | 7.6 |
| 21 | 6.9 | 6.7 | 7.1 | 7.1 | 9.0 |
| 22 | 12.1 | 11.0 | 10.9 | 12.1 | 11.2 |
| 23 | 6.2 | 6.3 | 6.0 | 7.8 | 10.2 |
| Avg(dE) | 8.0 | 8.0 | 8.1 | 8.9 | 9.4 |
First of all, it should be noted that the individual models added (DA for DeOldify and DDs for DDColor) performed worse than the individual models tested in the previous analysis (DS for DeOldify and DD for DDColor). Conversely all combinations of DeOldify and DDColor performed well. Confirming the positive impact on the final result, already observed in the previous analysis, obtained by combining the 2 models.