This paper deals with the recent trends of reseaches on intelligent image coding technology focusing on model-based analysis synthesis coding. By means of the intelligent image coding scheme, we will be able to realize epock-making ultra-low-rate image transmission and/or so-called value-added visual telecommunications. In order to categorize the various image coding systems and examine their potential applications in the future, an approach to define generations of image coding technologies is presented. The future generation coding systems include the model-based analysis synthesis coding and knowledge-based intelligent coding. The latter half of the paper will be devoted to the recent work of the authors on the model-based analysis-synthesis coding system for facial images.

As a basic tool for deriving sparse representation of a color image from its atomic-decomposition with a redundant dictionary, the authors have recently proposed a new kind of shrinkage technique, viz. color shrinkage, which utilizes inter-channel color dependence directly in the three primary color space. Among various schemes of color shrinkage, this paper particularly presents the soft color-shrinkage and the hard color-shrinkage, natural extensions of the classic soft-shrinkage and the classic hard-shrinkage respectively, and shows their advantages over the existing shrinkage approaches where the classic shrinkage techniques are applied after a color transformation such as the opponent color transformation. Moreover, this paper presents the applications of our color-shrinkage schemes to color-image processing in the redundant tight-frame transform domain, and shows their superiority over the existing shrinkage approaches.

It is a very important and intriguing problem in digital image-processing to decompose an input image into intuitively convincible image-components such as a structure component and a texture component, which is an inherently nonlinear problem. Recently, several numerical schemes to solve the nonlinear image-decomposition problem have been proposed. The use of the nonlinear image-decomposition as a pre-process of several image-processing tasks will possibly pave the way to solve difficult problems posed by the classic approach of digital image-processing. Since the new image-processing approach via the nonlinear image-decomposition treats each separated component with a processing method suitable to it, the approach will successfully attain target items seemingly contrary to each other, for instance invisibility of ringing artifacts and sharpness of edges and textures, which have not attained simultaneously by the classic image-processing approach. This paper reviews quite recently developed state-of-the-art schemes of the nonlinear image-decomposition, and introduces some examples of the decomposition-and-processing approach.

In this paper, subband image coding with symmetric biorthogonal wavelet filters is studied. In order to implement the symmetric biorthogonal wavelet basis, we use the Laplacian Pyramid Model (LPM) and the trigonometric polynomial solution method. These symmetric biorthogonal wavelet basis are used to form filters in each subband. Also coefficients of the filter are optimized with respect to the coding efficiency. From this optimization, we show that the values of a in the LPM generating kernel have the best coding efficiency in the range of 0.7 to 0.75. We also present an optimal bit allocation method based on considerations of the reconstruction filter characteristics. The step size of each subband uniform quantizer is determined by using this bit allocation method. The coding efficiency of the symmetric biorthogonal wavelet filter is compared with those of other filters: QMF, SSKF and Orthonormal wavelet filter. Simulation results demonstrate that the symmetric biorthogonal wavelet filter is useful as a basic means for image analysis/synthesis filters and can give better coding efficiency than other filters.

Very high resolution images with more than 2,000*2.000 pels will play a very important role in a wide variety of applications of future multimedia communications ranging from electronic publishing to broadcasting. To make communication of very high resolution images practicable, we need to develop image coding techniques that can compress very high resolution images efficiently. Taking the channel capacity limitation of the future communication into consideration, the requisite compression ratio will be estimated to be at least 1/10 to 1/20 for color signals. Among existing image coding techniques, the sub-band coding technique is one of the most suitable techniques. With its applications to high-fidelity compression of very high resolution images, one of the major problem is how to encode high frequency sub-band signals. High frequency sub-band signals are well modeled as having approximately memoryless probability distribution, and hence the best way to solve this problem is to improve the quantization of high frequency sub-band signals. From the standpoint stated above, the work herein first compares three different scalor quantization schemes and improved permutation codes, which the authors have previously developed extending the concept of permutation codes, from the aspect of quantization performance for a memoryless probability distribution that well approximates the real statistical properties of high frequency sub-band signals, and thus demonstrates that at low coding rates improved permutation codes outperform the other scalor quatization schemes and that its superiority decreases as its coding rate increases. Moreover, from the results stated above, the work herein, develops a rate-adaptive quantization technique where the number of bits assigned to each subblock is determined according to the signal variance within the subblock and the proper quantization scheme is chosen from among different types of quantization schemes according to the allocated number of bits, and applies it to the high-fidelity encoding of sub-band signals of very high resolution images to demonstrate its usefulness.

To make the model-based coding a practical method, new signal processing techniques other than fully-automatic image recognition should be studied. Also after having realized the model-based coding, another new signal processing technique to improve the performance of the model-based coding should be studied. Moreover non-coding functions related to the model-based coding can be embedded as additional features. The authors are studying the interactive model-based coding in order to achieve its practical realization, improve its performance and extend related non-coding functions. We have already proposed the basic concept of interactive model-based coding and presented an eyeglasses processing for a facial image with glasses to remove the frame for improving the model-based coding performance. In this paper, we focus on the 3-D motion detection algorithm in the interactive model-based coding. Previous works were mainly based on iterative methods to solve non-linear equations. A new motion detection algorithm is developed for interactive model-based coding. It is linear because the interactive operation generates more information and the environment of the applications limits the range of parameters. The depth parameter is first obtained by the fact that a line segment is invariant as to 3-D space transformation. Relation of distance between two points is utilized. The number of conditions is larger than that of the unknown variables, which allows to use least square method for obtaining stable solutions in the environment of the applications. Experiments are carried out using the proposed motion detection method and input noise problems are removed. Synthesized wireframe modified by eight parameters provides smooth and natural motion.

We have recently extended one of the conceptions of the lossless universal pattern-matching coding, viz. the concept of coding via copying, to multi-dimensional lossy coding, and applied the extended concept to intraframe compression of still images. The work herein applies the extended concept of lossy coding via copying to interframe low-rate video compression, thus developing a novel low-rate interframe PMIC (pattern-matching image coding) technique, which produces the effect of generalizing the definition of a search area used in the existing block-matching motion compensation. We have experimentally shown the performance gain provided by the generalization within the framework of lossy coding via copying, and demonstrated that the interframe PMIC technique is usefull and potential as a basic means for low-rate video compression.