Problem Of Image Compression Research Articles

PROJECTIVE NON-NEGATIVE MATRIX FACTORIZATION WITH APPLICATIONS TO FACIAL IMAGE PROCESSING

We propose a new variant of Non-negative Matrix Factorization (NMF), including its model and two optimization rules. Our method is based on positively constrained projections and is related to the conventional SVD or PCA decomposition. The new model can potentially be applied to image compression and feature extraction problems. Of the latter, we consider processing of facial images, where each image consists of several parts and for each part the observations with different lighting mainly distribute along a straight line through the origin. No regularization terms are required in the objective functions and both suggested optimization rules can easily be implemented by matrix manipulations. The experiments show that the derived base vectors are spatially more localized than those of NMF. In turn, the better part-based representations improve the recognition rate of semantic classes such as the gender or existence of mustache in the facial images.

ANALYSIS OF THE COMPRESSION RATIO AND QUALITY IN MEDICAL IMAGES

In the modern medical information systems documents are stored in the centralized storage. Choosing a proper, for the medical image storage, compression format is an important problem. This paper analyzes medical image compression in popular compression formats. For the compression format comparison there is proposed image quality evaluation algorithm based on the calculation of the mean exponent error value. Presented image quality evaluation experiment. Analyzed the distribution of the errors in medical images and explained the causes determining better than usual compression effect. Proposed complex solution of the medical image compression problem and specified com-pression format most suitable for the medical images.

Image Compression and Reconstruction Using pi t -Sigma Neural Networks

A high-order feedforward neural architecture, called pi t -sigma (? t ?) neural network, is proposed for lossy digital image compression and reconstruction problems. The ? t ? network architecture is composed of an input layer, a single hidden layer, and an output layer. The hidden layer is composed of classical additive neurons, whereas the output layer is composed of translated multiplicative neurons (? t -neurons). A two-stage learning algorithm is proposed to adjust the parameters of the ? t ? network: first, a genetic algorithm (GA) is used to avoid premature convergence to poor local minima; in the second stage, a conjugate gradient method is used to fine-tune the solution found by GA. Experiments using the Standard Image Database and infrared satellite images show that the proposed ? t ? network performs better than classical multilayer perceptron, improving the reconstruction precision (measured by the mean squared error) in about 56%, on average.

An adaptive incremental LBG for vector quantization

This study presents a new vector quantization method that generates codewords incrementally. New codewords are inserted in regions of the input vector space where the distortion error is highest until the desired number of codewords (or a distortion error threshold) is achieved. Adoption of the adaptive distance function greatly increases the proposed method's performance. During the incremental process, a removal–insertion technique is used to fine-tune the codebook to make the proposed method independent of initial conditions. The proposed method works better than some recently published efficient algorithms such as Enhanced LBG (Patane, & Russo, 2001) for traditional tasks: with fixed number of codewords, to find a suitable codebook to minimize distortion error. The proposed method can also be used for new tasks that are insoluble using traditional methods: with fixed distortion error, to minimize the number of codewords and find a suitable codebook. Experiments for some image compression problems indicate that the proposed method works well.

Image Compression by Layered Quantum Neural Networks

We have proposed the qubit neuron model as a new scheme in non-standard computing. Identification problems have been investigated on neural networks constructed by this qubit neuron model, and we have found high processing abilities of them. In this paper, we evaluate the performance of the quantum neural network of large size in image compression problems to estimate the utility for the practical applications comparing with the conventional network consists of formal neuron model.

Weighted centroid neural network for edge preserving image compression

An edge preserving image compression algorithm based on an unsupervised competitive neural network is proposed. The proposed neural network, the called weighted centroid neural network (WCNN), utilizes the characteristics of image blocks from edge areas. The mean/residual vector quantization (M/RVQ) scheme is utilized in this proposed approach as the framework of the proposed algorithm. The edge strength of image block data is utilized as a tool to allocate the proper code vectors in the proposed WCNN. The WCNN successfully allocates more code vectors to the image block data from edge area while it allocates less code vectors to the image black data from shade or non-edge area when compared to conventional neural networks based on VQ algorithm. As a result, a simple application of WCNN to an image compression problem gives improved edge characteristics in reconstructed images over conventional neural network based on VQ algorithms such as self-organizing map (SOM) and adaptive SOM.

Technique for design of two-channel approximately linear phase QMF filter bank and its application to image compression

The two-channel QMF filter bank based on allpass sections is one of the best known circuits for building up a multi-channel filter bank for signal compression. An analysis-synthesis combination can satisfy two of the three perfect reconstruction (PR) conditions. The third, the phase condition, can be met to any desired accuracy. However, when applied to images, PR is possible because non-causality can be allowed in the synthesis bank. The development of explicit formulae for the coefficients of such filters is considered. The resulting QMF designs can be used in a wavelet structure for image compression problems using rectangular or diamond symmetry. Several design examples are given, including a comparison of performance with FIR filter banks for such problems.

SMEM Algorithm for Mixture Models

We present a split-and-merge expectation-maximization (SMEM) algorithm to overcome the local maxima problem in parameter estimation of finite mixture models. In the case of mixture models, local maxima often involve having too many components of a mixture model in one part of the space and too few in another, widely separated part of the space. To escape from such configurations, we repeatedly perform simultaneous split-and-merge operations using a new criterion for efficiently selecting the split-and-merge candidates. We apply the proposed algorithm to the training of gaussian mixtures and mixtures of factor analyzers using synthetic and real data and show the effectiveness of using the split-and-merge operations to improve the likelihood of both the training data and of held-out test data. We also show the practical usefulness of the proposed algorithm by applying it to image compression and pattern recognition problems.

Fast solving method of fuzzy relational equation and its application to lossy image compression/reconstruction

A fast solving method of the solution for max continuous t-norm composite fuzzy relational equation of the type G(i, j)=(R/sup T//spl square/A/sub i/)/sup T//spl square/B/sub j/, i=1, 2, ..., I, j=1, 2, ..., J, where A/sub i//spl isin/F(X)X={x/sub 1/, x/sub 2/, ..., x/sub M/}, Bj/spl isin/F(Y) Y={y/sub 1/, y/sub 2/, ..., y/sub N/}, R/spl isin/F(X/spl times/Y), and /spl square/: max continuous t-norm composition, is proposed. It decreases the computation time IJMN(L+T+P) to JM(I+N)(L+P), where L, T, and P denote the computation time of min, t-norm, and relative pseudocomplement operations, respectively, by simplifying the conventional reconstruction equation based on the properties of t-norm and relative pseudocomplement. The method is applied to a lossy image compression and reconstruction problem, where it is confirmed that the computation time of the reconstructed image is decreased to 1/335.6 the compression rate being 0.0351, and it achieves almost equivalent performance for the conventional lossy image compression methods based on discrete cosine transform and vector quantization.

Line-based, reduced memory, wavelet image compression

This paper addresses the problem of low memory wavelet image compression. While wavelet or subband coding of images has been shown to be superior to more traditional transform coding techniques, little attention has been paid until recently to the important issue of whether both the wavelet transforms and the subsequent coding can be implemented in low memory without significant loss in performance. We present a complete system to perform low memory wavelet image coding. Our approach is "line-based" in that the images are read line by line and only the minimum required number of lines is kept in memory. There are two main contributions of our work. First, we introduce a line-based approach for the implementation of the wavelet transform, which yields the same results as a "normal" implementation, but where, unlike prior work, we address memory issues arising from the need to synchronize encoder and decoder. Second, we propose a novel context-based encoder which requires no global information and stores only a local set of wavelet coefficients. This low memory coder achieves performance comparable to state of the art coders at a fraction of their memory utilization.

Centroid neural network for unsupervised competitive learning

An unsupervised competitive learning algorithm based on the classical -means clustering algorithm is proposed. The proposed learning algorithm called the centroid neural network (CNN) estimates centroids of the related cluster groups in training date. This paper also explains algorithmic relationships among the CNN and some of the conventional unsupervised competitive learning algorithms including Kohonen's self-organizing map (SOM) and Kosko's differential competitive learning (DCL) algorithm. The CNN algorithm requires neither a predetermined schedule for learning coefficient nor a total number of iterations for clustering. The simulation results on clustering problems and image compression problems show that CNN converges much faster than conventional algorithms with compatible clustering quality while other algorithms may give unstable results depending on the initial values of the learning coefficient and the total number of iterations.

GENETIC ALGORITHM IN UNCERTAIN ENVIRONMENTS FOR SOLVING STOCHASTIC PROGRAMMING PROBLEM

Many real problems with uncertainties may often be formulated as Stochastic Programming Problem. In this study, Genetic Algorithm (GA) which has been recently used for solving mathematical programming problem is expanded for use in uncertain environments. The modified GA is referred as GA in uncertain environments (GAUCE). In the method, the objective function and/or the constraint are fluctuated according to the distribution functions of their stochastic variables. Firstly, the individual with highest frequency through all generations is nominated as the individual associated with the solution presenting the best expected value of objective function. The individual with highest frequency is associated with the solution by GAUCE. The proposed method is applied to Stochastic Optimal Assignment Problem, Stochastic Knapsack Problem and newly formulated Stochastic Image Compression Problem. Then, it has been proved that the solution by GAUCE has excellent agreement with the solution presenting the best expected value of objective function, in cases of both Stochastic Optimal Assignment Problem and Stochastic Knapsack Problem. GAUCE is also successfully applied to Stochastic Image Compression Problem where the coefficients of discrete cosine transformation are treated as stochastic variables.

High-performance compression of visual information-a tutorial review. I. Still pictures

Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research efforts have been devoted to the problem of image compression in the last two decades. Two different compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image. Applications requiring this type of compression include medical imaging and satellite photography. For applications such as video telephony or multimedia applications, some loss of information is usually tolerated in exchange for a high compression ratio. In this two-part paper, the major building blocks of image coding schemes are overviewed. Part I covers still image coding, and Part II covers motion picture sequences. In this first part, still image coding schemes have been classified into predictive, block transform, and multiresolution approaches. Predictive methods are suited to lossless and low-compression applications. Transform-based coding schemes achieve higher compression ratios for lossy compression but suffer from blocking artifacts at high-compression ratios. Multiresolution approaches are suited for lossy as well for lossless compression. At lossy high-compression ratios, the typical artifact visible in the reconstructed images is the ringing effect. New applications in a multimedia environment drove the need for new functionalities of the image coding schemes. For that purpose, second-generation coding techniques segment the image into semantically meaningful pairs. Therefore, parts of these methods have been adapted to work for arbitrarily shaped regions. In order to add another functionality, such as progressive transmission of the information, specific quantization algorithms must he defined. A final step in the compression scheme is achieved by the codeword assignment. Finally, coding results are presented which compare state-of-the-art techniques for lossy and lossless compression. The different artifacts of each technique are highlighted and discussed. Also, the possibility of progressive transmission is illustrated.

Human visual model and wavelets for multi-resolution image coding

In this paper we present the results of a research activity which addresses the problem of high-quality image compression. The approach consists of embedding a model of the human visual system into the framework of the biorthogonal wavelet expansion. After a brief overview on the application of wavelets on image compression and a comparison with other transform-based approaches, we describe the mathematical aspect of the algorithm and the coding method used in the compression scheme. Some examples of the experimental results we have obtained end the paper.

Neural network approaches to image compression

This paper presents a tutor a overview of neural networks as signal processing tools for image compression. They are well suited to the problem of image compression due to their massively parallel and distributed architecture. Their characteristics are analogous to some of the features of our own visual system, which allow us to process visual information with much ease. For example, multilayer perceptions can be used as nonlinear predictors in differential pulse-code modulation (DPCM). Such predictors have been shown to increase the predictive gain relative to a linear predictor. Another active area of research is in the application of Hebbian learning to the extraction of principal components, which are the basis vectors for the optimal linear Karhunen-Loeve transform (KLT). These learning algorithms are iterative, have some computational advantages over standard eigendecomposition techniques, and can be made to adapt to changes in the input signal. Yet another model, the self-organizing feature map (SOFM), has been used with a great deal of success in the design of codebooks for vector quantization (VQ). The resulting codebooks are less sensitive to initial conditions than the standard LBG algorithm, and the topological ordering of the entries can be exploited to further increasing the coding efficiency and reducing the computational complexity.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Performance evaluation for four classes of textural features

Textural features for pattern recognition are compared. The problem addressed is to determine which features optimize classification rate. Such features may be used in image segmentation, compression, inspection, and other problems in computer vision. Many textural features have been proposed in the literature. No large-scale objective comparative study has appeared. The goal is comparing and evaluating in a quantitative manner four types of features, namely Markov Random Field parameters, multi-channel filtering features, fractal based features, and co-occurrence features. Performance is assessed by the criterion of classification error rate with a Nearest Neighbor classifier and the Leave-One-Out estimation method using forward selection. Four types of texture are studied, two synthetic (fractal and Gaussian Markov Random Fields) and two natural (leather and painted surfaces). The results show that co-occurrence features perform best followed by the fractal features. However, there is no universally best subset of features. The feature selection task has to be performed for each specific problem to decide which feature of which type one should use.

Parallel structure based on neural networks for image compression

The problem of image compression via neural networks (NNs) is considered. A parallel architecture is presented in which different parts of the image are processed by different NNs according to their complexity. Simulations are presented which show the simplicity and power of this approach.