Question

I have a device that is taking TV screenshots at precise times (it doesn’t take incomplete frames).

Still this screenshot is an interlace image made from two different original frames.

Now, the question is if/how is possible to identify which of the lines are newer/older.

I have to mention that I can take several sequential screenshots if needed.

Answer 1

Take two screenshots one after another, yielding a sequence of two images (1,2). Split each screenshot into two fields (odd and even) and treat each field as a separate image. If you assume that the images are interlaced consistently (pretty safe assumption, otherwise they would look horrible), then there are two possibilities: (1e, 1o, 2e, 2o) or (1o, 1e, 2o, 2e). So at the moment it’s 50-50.

What you could then do is use optical flow to improve your chances. Say you go with the
first option: (1e, 1o, 2e, 2o). Calculate the optical flow f1 between (1e, 2e). Then calculate the flow f2 between (1e, 1o) and f3 between (1o,2e). If f1 is approximately the same as f2 + f3, then things are moving in the right direction and you’ve picked the right arrangement. Otherwise, try the other arrangement.

Optical flow is a pretty general approach and can be difficult to compute for the entire image. If you want to do things in a hurry, replace optical flow with video tracking.

EDIT

I’ve been playing around with some code that can do this cheaply. I’ve noticed that if 3 fields are consecutive and in the correct order, the absolute error due to smooth, constant motion will be minimized. On the contrary, if they are out of order (or not consecutive), this error will be greater. So one way to do this is two take groups of 3 fields and check the error for each of the two orderings described above, and go with the ordering that yielded the lower error.

I’ve only got a handful of interlaced videos here to test with but it seems to work. The only down-side is its not very effective unless there is substantial smooth motion or the number of used frames is low (less than 20-30).

Here’s an interlaced frame:

Here’s some sample output from my method (same frame):

The top image is the odd-numbered rows. The bottom image is the even-numbered rows. The number in the brackets is the number of times that image was picked as the most recent. The number to the right of that is the error. The odd rows are labeled as the most recent in this case because the error is lower than for the even-numbered rows. You can see that out of 100 frames, it (correctly) judged the odd-numbered rows to be the most recent 80 times.