Camera Algorithms are an essential feature of the camera system operation. Captured images are significantly modified before being presented to the user. The algorithms can be divided roughly into quality related, sensor and camera module specific and special effects as following figure describes. Very often, there is a strong interdependency between algorithms and therefore, changes in one algorithm changes the characteristics of another. The combinations of different algorithm parameters are numerous and the testing of each case is very demanding.
A good example of a challenging algorithm to test is the auto white balance. Various algorithms and camera settings influence the color balancing of the image and the final result may be as in attached image. The leftmost image has the correct colors while the right has serious color defects.
Other common issues of digital camera modules can include, exposure and focus problems, noise artifacts and lens specific errors, like lens shading and distortion.
Even if some camera algorithm defects can be detected by human eye, objective testing and repetitive measurement is very demanding. Furthermore, increasing the number of camera algorithms and their interdependencies increases the test load significantly.
SoMA provides a solution, which analyzes mathematically the captured images and compares the results to reference images. This brings strict objectivity to the testing. When mathematical analysis is executed in the automated test environment of SoMA, testing is very efficient and reproducible.