Imaging resolution has been standing as a core parameter in various applications of vision. Mostly, high resolutions are desirable or essential for many applications, e.g., in most remote sensing systems, and therefore much has been done to achieve a higher resolution of an image based on one or a series of images of relatively lower resolutions. On the other hand, lower resolutions are also preferred in some cases, e.g., for displaying images in a very small screen or interface. Accordingly, algorithms for image upsampling or downsampling have also been proposed. In the above algorithms, the downsampled or upsampled (super-resolution) versions of the original image are often taken as test images to evaluate the performance of the algorithms. However, there is one important question left unanswered: whether the downsampled or upsampled versions of the original image can represent the low-resolution or high-resolution real images from a camera? To tackle this point, the following works are carried out: 1) a multiresolution camera is designed to simultaneously capture images in three different resolutions; 2) at a given resolution (i.e., image size), the relationship between a pair of images is studied, one gained via either downsampling or super-resolution, and the other is directly captured at this given resolution by an imaging device; and 3) the performance of the algorithms of super-resolution and image downsampling is evaluated by using the given image pairs. The key reason why we can effectively tackle the aforementioned issues is that the designed multiresolution imaging camera can provide us with real images of different resolutions, which builds a solid foundation for evaluating various algorithms and analyzing the images with different resolutions, which is very important for vision.