MSU Device Testing - determination of the 3D devices’ characteristics: About the project

Table of contents

Introduction

The 3D devices become available to a large number of people over the past few years. The 3D technology gets more and more distributed and advertised. But still there are many problems for producer and advertisers. One of the problems with viewing high-quality 3D video is the problem with display devices (because even the best image can become a nightmare for a viewer if it is demonstrated on poor equipment)

Example problem
The example of the display problem. Stereo system, consisting of two projectors. The problem with one of the lamps.

Most of the 3D devices can not provide comfortable and high-quality displaying, and thus can both harm human health (cause eye fatigue, headaches, etc) and influence public opinion on the whole 3D video area. However, some problem of bad displaying can be diagnosed and corrected, thus making viewing more comfortable. The producer often do not fully provide the end user with information about the technical characteristics and real capabilities of their devices.

The goal and the tasks

How test works?

Prepared the test images system, which allows to determine many important characteristics of 3D device.

Checking the characteristics of the equipment is as follows:

  1. The test images are displaying on the tested equipment
  2. Taking pictures of the images obtained using the 3D device
  3. Computing the characteristics of the test equipment by means of the computer vision
  4. The results are entering into the database

    As a result - the world have become a little better!

18 Mar 2013
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.
Geometric distortions analysis and correction
Production of low-budget movies is prone to errors. Our method automatically corrects rotation and scale mismatch.
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?
Site structure