A Novel Method for Estimating the Spatial Frequency Characteristics of Cameras Based on Generative Random Sequences

Cameras have become ubiquitous in our daily lives. The task of measuring and verifying the camera’s declared characteristics is of increasing importance. Current measurement methods range from subjective assessments using a large number of experts to more stringent characteristics, such as the determination of the modulation transfer function. Methods used last ten years have proven successful and reliable results. However, the question of how the current computational methods improve the performance of the camera has received much less attention, especially in the conditions of the test. A significant problem is a fact that the measuring instruments used in the test are standardized and do not change between measurements. In this paper, new a generative table-based measurement approach that allows you to create random initial measurement conditions. This new method opens up opportunities for improving the quality assessment of the camera’s work and providing real characteristics. The method is based on random sequences and cannot be deceived by processing algorithms inside the current software of the camera. The method is suitable to assess compression implementations in closed systems