Acoustic echo cancellers utilizing adaptive finite impulse response (FIR) filters conventionally incorporate learning identification (LI) also called the normalized least mean square (NLMS) adaptation algorithm. This echo cancellers have a serious problem in that colored signal input and incremental tap length degrade their echo cancellation characteristics. In this paper, a new adaptation algorithm combining reductive correlation and variable loop gain methods is proposed. The proposed algorithm is shown in the simulation to have good convergence characteristics compared to conventional methods. Furthermore, a sub-band echo canceller is proposed in which the input signal is separated into several sub-band signals in the frequency domain. Using this echo canceller, the computation quantity per sampling period in a processing module can be greatly reduced. Finally, the relationship between the overall echo cancellation performance and the characteristics of the sub-band filters is clarified.

Digitizing high-quality microscopic images and developing input/output technology for displaying those results is critical to telepathology in which pathological microscopic images are transferred to remote locations where they are diagnosed by specialists. This paper will discuss the results achieved by directly digitizing (nonfilm process) pathological microscopic images at a 2k x 2k resolution, and then using a super-high-definition imaging system to analyze their signals and evaluate compression performance. We will start off by digitizing samples that a pathologist will actually use in making a diagnosis, and then analyze their color distribution and spatial frequencies characteristics by comparing them to general images. This will make it apparent that such pathological images characteristically contain high spatial frequency in their chrominance components. We will also discuss the evaluation results of color differences for L*a*b* space and compression ratios achieved when using JPEG to encode pathological images. We will also present a subjective evaluation of the influence subsampling of chrominance components has on image quality.

Simple chest X rays on film are the most common type of image in medical diagnosis. However, amongst the various types of medical X-ray images, they require the highest level of display quality due to the fact that the body structures they capture on film have varying degrees of permeability to X rays. Conventional high-definition digital display technology has not always been able to match the quality of such film images. This has been a major impediment against progress toward the complete digitization of simple chest X rays. The intent of this paper is to examine that, when applied to medical diagnosis of chest X rays, super-high-definition (SHD) images (digital images with resolution exceeding that of HDTV) are capable of producing a level of quality of diagnostic accuracy on a par with conventional film images. We will start out by seeking out the overall transmission characteristics of a system that uses digital radiography and a film digitizer to digitize images. We will then derive gray-scale transform characteristics based on the luminance linear method for approximating, as closely as possible on a CRT, film images on a light box that have wide dynamic range and high luminance. Finally, we will present the results of image evaluation experiments using high-definition CRT monitors. These results indicate that conventional film images and those on super-high-definition CRT monitors have nearly the same quality. They will also show that the contrast mapping selected by radiologists and theoretical luminance linear characteristics were almost the same except in low-luminance regions. We will also discuss radiologists' comments on CRT monitors after they participated in the evaluation experiment.

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