This paper discusses the conversion of spatial resolution (pixel density) and amplitude resolution (levels of brightness) for multilevel images. A source image is sampled by an image scanner or a video camera, and a converted image is printed by a printer with the capability of higher spatial but lower amplitude resolution than the image input device. In the proposed method, the impulse response of the scanner sensor is modeled to obtain pixel values from the convolution of the impulse and the image signal. Discontinuous areas (edge) of the original image are detected locally according to the impulse model and neighbouring pixel values. The edge route is estimated which gives the pixel values for the output resolutions. Comparison of the proposed method with two conventional methods, reciprocal distance weight interpolation and pixel replication, shows higher edge quality for the proposed method.

We have proposed a novel call admission control (CAC) method for maximizing total user satisfaction in a heterogeneous traffic network and showed their effectiveness by using the optimal threshold from numerical analysis [1],[2]. With these CAC methods, it is assumed that only selfish users exist in a network. However, we need to consider the possibility that some cooperative users exist who would agree to reduce their requested bandwidth to improve another user's Quality of Service (QoS). Under this assumption, conventional CAC may not be optimal. If there are cooperative users in the network, we need control methods that encourage such user cooperation. However, such “encourage” control methods have not yet been proposed. Therefore, in this paper, we propose novel CAC methods for cooperative users by using queueing theory. Numerical analyses show their effectiveness. We also analyze the characteristics of the optimal control parameter of the threshold.

This paper discusses a new image resolution conversion method which converts not only spatial resolution but also amplitude resolution. This method involves considering impulse responses of image devices and human visual characteristics, and can preserve high image quality. This paper considers a system that digitizes the multilevel input image with high spatial resolution and low amplitude resolution using an image scanner, and outputs the image with low spatial resolution and high amplitude resolution on a CRT display. The algorithm thus reduces the number of pixels while increasing the number of brightness levels. Since a CRT display is chosen as the output device, the distribution of each spot in the display, which is modeled as a Gaussian function, is taken as the impulse response. The output image is then expressed as the summation of various amplitudes of the impulse response. Furthermore, human visual perception, which bears a nonlinear relationship to the spatial frequency component, is simplified and modeled with a cascade combination of low-pass and high-pass filters. The output amplitude is determined so that the error between the output image and the input image, after passing through the visual perception filter, is minimized. According to the results of a simulation, it is shown that image quality can be largely preserved by the proposed method, while significant image information is lost by conventional methods.