The goal of personal-style handwriting synthesis is to produce texts in the same style as an individual writer by analyzing the writer's samples of handwriting. The difficulty of handwriting synthesis is that the output should have the characteristics of the person's handwriting as well as looking natural, based on a limited number of available examples. We develop a synthesis algorithm which produces handwriting that exhibits naturalness based on the probabilistic character model.

This paper presents a method of scalable lossless image compression by means of lossy coding. A progressive decoding capability and a full decoding for the lossless rendition are equipped with the losslessly encoded bit stream. Embedded coding is applied to large-amplitude coefficients in a wavelet transform domain. The other wavelet coefficients are encoded by a context-based entropy coding. The proposed method slightly outperforms JPEG-LS in lossless compression. Its rate-distortion performance with respect to progressive decoding is close to that of JPEG2000. The spatial scalability with respect to resolution is also available.

In this paper, we investigate minimum mean absolute error (mmae) predictors for lossless image coding. In some prediction-based lossless image coding systems, coding performance depends largely on the efficiency of predictors. In this case, minimum mean square error (mmse) predictors are often used. Generally speaking, these predictors have a problem that outliers departing very far from a regression line are conspicuous enough to obscure inliers. That is, in image compression, large prediction errors near edges cause the degradation of the prediction accuracy of flat areas. On the other hand, mmae predictors are less sensitive to edges and provide more accurate prediction for flat areas than mmse predictors. At the same time, the prediction accuracy of edge areas is brought down. However, the entropy of the prediction errors based on mmae predictors is reduced compared with that of mmse predictors because general images mainly consist of flat areas. In this study, we adopt the Laplacian and the Gaussian function models for prediction errors based on mmae and mmse predictors, respectively, and show that mmae predictors outperform conventional mmse-based predictors including weighted mmse predictors in terms of coding performance.

In order to prepare the health care industry for an increasingly aging society, a ubiquitous health care infrastructure is certainly needed. In a ubiquitous computing environment, it is important that all applications and middleware should be executed on an embedded system. To provide personalized health care services to users anywhere and anytime, a context-aware framework should convert low-level context to high-level context. Therefore, ontology and rules were used in this research to convert low-level context to high-level context. In this paper, we propose context modeling and context reasoning in a context-aware framework which is executed on an embedded wearable system in a ubiquitous computing environment for U-HealthCare. The objective of this research is the development of the standard ontology foundation for health care services and context modeling. A system for knowledge inference technology and intelligent service deduction is also developed in order to recognize a situation and provide customized health care service. Additionally, the context-aware framework was tested experimentally.

Natural, continuous tone images have a very important property of high correlation of adjacent pixels. Images which we wish to compress are usually non-stationary and can be reasonably modeled as smooth and textured areas separated by edges. This property has been successfully exploited in LOCO-I and CALIC by applying gradient based predictive coding as a major de-correlation tool. However, they only examine the horizontal and vertical gradients, and assume the local edge can only occur in these two directions. Their over-simplified assumptions hurt the robustness of the prediction in higher complex areas. In this paper, we propose an accurate gradient selective prediction (AGSP) algorithm which is designed to perform robustly around any type of image texture. Our method measures local texture information by comparison and selection of normalized scalar representation of the gradients in four directions. An adaptive predictor is formed based on the local gradient information and immediate causal pixels. Local texture properties are also exploited in the context modeling of the prediction error. The results we obtained on a test set of several standard images are encouraging. On the average, our method achieves a compression ratio significantly better than CALIC without noticeably increasing of computational complexity.

A simple and efficient context modeling technique (CM-WDR) is proposed to improve the performance of the wavelet difference reduction (WDR) algorithm for image compression. The CM-WDR employs an adaptive scanning order by context modeling. The PSNR improvement over WDR ranges from 0.1 to 1.5 dB at various bitrates.