Automatic sharpness mismatch detection and compensation in stereo


Enlarged fragment of left and right views with visible sharpness mismatch from “Jack the Giant Slayer”

A lot of S3D movies contain artifacts despite large budgets and modern post-processing methods. It is caused by insufficiency in automatization of post-processing and high rate of human errors.

Differences in sharpness between views are usually caused by incorrectly calibrated cameras. While watching movies with sharpness mismatch the spectator may lose sense of 3D, which is the primary goal of stereo, or even get a headache.

Proposed method

The algorithm estimates the amount of local sharpness mismatch for each stereo pair, trying to fix it if possible.

The steps of the algorithm:


The “Driving Angry” movie: left — input stereo pair, right — corrected stereo pair

We compared our algorithm with a commercial plug-in for Nuke — Ocula 3.0 FocusMatcher on a set of 8 stereo sequences.

Pros over Ocula 3.0:

Results of the proposed algorithm

Results of comparison with Ocula 3.0 (FocusMatching)

Pictures below illustrate the differences between proposed algorithm and FocusMatcher on a single stereo pair. It is notable that FocusMarcher corrupts the background of semi-transparent objects (red boxes), while proposed algorithm doesn’t have such an effect.

Input views from “Driving Angry”

Result of proposed algorithm (corrected sharpness mismatch, no new artifacts introduced)

Ocula 3.0's result (red boxes contain produced artifacts)


27 May 2019
See Also
MSU Deinterlacer Benchmark 2020
MSU 3D-video Quality Analysis. Report 10
Detection of stereo window violation
How to find objects that are present only in one view?
Depth continuity estimation in S3D video
How smooth is the depth transition between scenes?
Detection of 3D movie scenes shot on converged axes
Another cause of headaches when watching 3D movies.
Parallax range estimation in S3D video
The parallax range should be both comfortable and entertaining for spectators.
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