This paper introduces a practical color filter array (CFA) interpolation technique. Among the many technologies proposed in this field, the inter-color methods that exploit correlation between color planes generally outperform the intra-color approaches. We have found that the filtering direction, e.g., horizontal or vertical, is among the most decisive factors for the performance of the CFA interpolation. However, most of the state-of-the-art technologies are not flexible enough in determining the filtering direction. For example, filtering only in the upper direction is not usually supported. In this context, we propose an inter-color CFA interpolation using a local map called unified geometry map (UGM). In this method, the filtering direction is determined based on the similarity of the local map data. Thus, it provides more choices of the filtering directions, which enhances the probability of finding the most appropriate direction. It is confirmed through simulations that the proposal outperforms the state-of-the-art algorithms in terms of objective quality measures. In addition, the proposed scheme is as inexpensive as the conventional methods with regard to resource consumption.

In this paper, a novel chrominance up-sampling algorithm for color post-processing is described. This scheme exploits so-called inter-chrominance coherence, i.e., luminance and chrominance signals share the same structural information. Usually luminance has higher spatial resolution than chrominance in compression coding standards. So the idea is to up-sample chrominance signals using the structural information extracted from the luminance.

Discrete cosine transform (DCT) coding has been proven to be an efficient means of image compression coding. A lot of efforts have been made to improve the coding efficiency of DCT based coding. This paper presents an adaptive DCT coding based on geometrical edge representation. This scheme is designed to properly exploit the correlation between edge direction and distribution of DCT coefficients. Edges are extracted from original images first. Then, sub-optimal block-size and scanning order are determined at each block based on the extracted edges. In this way an adaptive DCT scheme taking account of local characteristics of image can be achieved. It is shown through the simulations that the proposed algorithm outperforms a conventional coding scheme in terms of coding efficiency by 10-15%.

While high compression ratio has been achieved using recently developed image coding algorithm, the noise removal technique is considered as an important subject. This still holds for very low bitrate video coding, that is, MPEG-4 has defined it as a core experiment which is mainly concerned with block based discrete cosine transform (DCT) coding such as H.263 and MPEG-1. This paper describes a novel and practical technique which attempts to accomplish both noise suppression and detail preservation at the same time. Some of the conventional adaptive filters are designed to search a homogeneous region among the predetermined polygonal subregions, then to apply a smoothing operation within the selected subregion. It shall be, however noted that sometimes the predetermined subregion finally selected may still be hererogeneous. This fact leads us to a novel idea; instead of examining the predetermined regions, define a lot more flexible region likely to be homogeneous. In order to achieve this, we introduce the binary index. each pixel is classified into either the lower intensity group or higher intensity group based on a local statistics. Then a smoothing operation is applied within the pixels having the same group index as the pixel to be processed. Thus our scheme can search a homogeneous region appropriately. The adaptive smoothing adopted in the proposed scheme is also designed to be consistent with an important property of human visual system, i.e., the spatial masking. noise visibility decreases at spatial details such as edges and textures. Another advantage is that it can be realized with significantly low computations. The simulation results show that his approach can suppress the visible artifacts while retaining the fine details such as edge and texture.

Recently, control theory using machine learning, which is useful for the control of unknown systems, has attracted significant attention. This study focuses on such a topic with optimal control problems for unknown nonlinear systems. Because optimal controllers are designed based on mathematical models of the systems, it is challenging to obtain models with insufficient knowledge of the systems. Kernel functions are promising for developing data-driven models with limited knowledge. However, the complex forms of such kernel-based models make it difficult to design the optimal controllers. The design corresponds to solving Hamilton-Jacobi (HJ) equations because their solutions provide optimal controllers. Therefore, the aim of this study is to derive certain kernel-based models for which the HJ equations are solved in an exact sense, which is an extended version of the authors' former work. The HJ equations are decomposed into tractable algebraic matrix equations and nonlinear functions. Solving the matrix equations enables us to obtain the optimal controllers of the model. A numerical simulation demonstrates that kernel-based models and controllers are successfully developed.