Geometric distortions analysis and correction


Production of low-budget modern S3D movies is prone to errors — bad camera calibration, lack of synchronization. Such errors cause distortions — the left and right video streams may be out of sync, have color differences and geometric distortions. These problems make viewing uncomfortable and in some cases cause a headache. Distortion correction enhances the movie’s quality and increases viewing comfort. The following distortions are analyzed:

Rotation mismatch (degrees)

Scale mismatch (percents)

Vertical disparity (percents of frame width)

We have developed a method for evaluation and correction of these inconsistencies.

Distortion estimation and correction

The input data should be two time-synchronized videos of left and right camera views. The algorithm is as follows:


Distortion estimation

Testing of distortion estimation was performed on multiple scenes from the stereo version of “Titanic”. As the S3D version of this movie was produced via conversion, there are no geometric distortions. We introduced artificial distortions and compared the result of our algorithm with true values.

  True value Algorithm’s estimate
Rotation 0.5 0.498—0.503
Scaling 0.0182 0.0177—0.0186
Vertical disparity 5.3 5.2

Distortion correction

We have compared distortions after correction by the proposed algorithm and two commercial products — Ocula Vertical Aligner and Nuke: YUVSoft Stereo Correction. Post-correction distortions were estimated using our method.

  Our method Ocula Stereo Correction
Rotation 0 0.0—0.5 0.0
Scaling 0 0.0000—0.0004 0.0175—0.0185
Vertical disparity 0 0.0 -0.15

Both of the commercial products were outperformed by our algorithm. In the case of Ocula Vertical Aligner the rotation distortion of the corrected video linearly decreases for the first 150 frames, while our algorithm fully corrects the distortion.

Movie quality assessment

We have compared the quality of 105 stereoscopic movies of different types with our distortion estimation algorithm — movies that have been shot with stereo rigs, converted from 2D and created using computer graphics. Recent movies generally show improved quality.


06 May 2020
See Also
Call for HEVC codecs 2019
Fourteen modern video codec comparison
Parallax range estimation in S3D video
Parallax determines the depth of S3D movies. The range of parallaxes should be both comfortable and entertaining for spectators.
Automatic detection of artifacts in converted S3D videos
Our set of algorithms detects edge sharpness mismatch, cardboard effect, and crosstalk noticeability.
Temporal shift estimation for stereoscopic videos
How to take into account geometric distortions in the estimation of the temporal shift?
Neural network-based algorithm for classification of stereoscopic video by the production method
What method was used to create the 3D scene?
Perspective distortions estimation
How to detect a mismatch in the vertical position of the cameras?
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