## 1 Introduction  
Phone cameras released in the early 2000s had a very large pixel size because of the low number of pixels [1]. For this reason, tolerance for the sensor assembly error was large, and no serious issue occurs without needing to correct the change in the image surface according to the object distance. However, the phone camera installed on the body of the latest phones has a very high number of pixels. Therefore, correcting the change in the image surface is necessary according to the distance of the object. This image-surface correction is called a focus adjustment, which is a technique commonly used in compact cameras and interchangeable lenses [2]. However, because the phone camera is very small and light, it needs to perform focusing by moving the entire optical system [3]. Several studies on such phone cameras have been conducted. After focusing on the optical system, a study was conducted on the equipment that automatically checks the uniformity of the image and various defects in the optical system [3; 4; 5; 6]. Mostly, the captured images of compact camera modules assembled onto a charge-coupled device or complementary metal-oxide-semiconductor package, were automatically checked to detect some errors due to human operators. The proposed inspection processes that use a specialized image-processing algorithm were designed to reduce the overall assembly time and defects for compact camera module assembly. In addition, a real-time non-contact eccentric-error-measurement system that uses an optical setup without a rotating and clamping mechanism [7] and a method for detecting foreign substances on the lens surface were also proposed [8; 9]. The system could assess the 2D surface defects and non-uniformity of the 3D film thickness of specific materials using pattern recognition and morphological techniques. Because distinguishing the defect between the intensity of the defect itself and the background is difficult, an image-processing algorithm that uses multiple Laplacian of Gaussian masks were implemented to detect the defects of the micro lens. Moreover, a dynamic focus region from several narrow region of interests across the vision screen was used to increase the sharpness and determine the focal distance and focal point for better image acquisition [10]. Various computation methods that use the gray-level difference, Tenengrad measurement, correlation evaluation, statistical approaches, Fourier transform, wavelet, and edge-based measurement have helped to reduce the processing time and increase the sharpness [10]. The computation methods could be obtained from a flat surface at a single focal distance. Multi-focus application from the synthesis of differently focused images has been investigated to evaluate the sharpness. However, these studies did not focus on the measurement of the resolution of optical systems. In addition, a complex algorithm-based inspection procedure was automatically performed, but detecting the defects, error status, and accuracy increment would take time. In the present paper, we propose a method that can measure the resolution according to the change in the object distance without moving the resolution chart. One of the common methods of checking a phone camera is to take a target, such as an ISO 12,233 resolution chart, using an optical system for the testing, and to check the various images inside the chart [11]. At this time, the resolution can change according to the object distance of the tested optical system so that it can also be evaluated. To perform this process, the resolution chart should be placed on the actual object distance to be evaluated. Recently, phone cameras have been capable of auto-focusing. Thus, the resolution chart should be moved during the resolution measurement. In this case, the size of the resolution chart must be varied according to its location. Figure 1 shows the inspection principle of mobile phone camera optics.