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Conference on Applications of Artificial Neural Networks in image Processing II

作者： Murshed, NA Bortolozzi, F Sabourin, R Pontificia Univ. Catolica do Parana (Brazil) Ecole de Technologie Superieure/Montreal (Canada)

ISBN: (纸本)0819424412

This work proposes a method for using an Identity-Mapping Backpropagation (IMBKP) Neural Network for binary image compression, aimed at reducing the dimension of the feature vector in a NN-based pattern recognition system. In the proposed method, the IMBKP network was trained with the objective of achieving good reconstruction quality and a reasonable amount of image compression. This criteria is very important, when using binary images as feature vectors. Evaluation of the proposed network was performed using 800 images of handwritten signatures. The lowest and highest reconstruction errors were, respectively, 3.05 x 10(-3)% and 0.01%. The proposed network can be used to reduce the dimension of the input vector to a NN-based pattern recognition system without almost any degradation and, yet, with a good reduction in the number of input neurons.

关键词： identity mapping backpropagation neural network image compression binary image compression dimensionality reduction

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New Rectangular Partitioning Methods for Loss less binary image compression

New Rectangular Partitioning Methods for Loss less Binary Im...

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IEEE 10th International Conference on Signal Processing

作者： Kafashan, M. Hosseini, H. Beygiharchegani, S. Pad, P. Marvasti, F. Sharif Univ Technol Dept Elect Engn ACRI Tehran Iran

ISBN: (纸本)9781424458981

In this paper, we propose two lossless compression techniques that represent a two dimensional Run-length Coding which can achieve high compression ratio. This method works by partitioning the block regions of the input image into rectangles instead of working by runs of adjacent pixels, so it is found to be more efficient than ID RLE Run-length Coding for transmitting texts and image. In the first method, length and width of consecutive black and white rectangles are transmitted. The idea of this method is new and it can be very effective for some images which have large blocks of black or white pixels. But in the second method only black rectangles are considered in order to transmit and an intelligent procedure is exploited to encoding the image. The first method is faster and simpler to implement than the second one but its compression ratio is lesser. Our proposed second scheme is more suitable for text compression and has outperformed the existence partitioning methods. Therefore, if we used these partitioning methods instead of previous partitioning methods in well-known lossless compression techniques, we would have a better compression ratio.

关键词： Digital image processing binary image compression rectangular partitioning Arithmetic encoding

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A near minimum sparse pattern coding based scheme for binary image compression

A near minimum sparse pattern coding based scheme for binary...

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IEEE International Conference on image Processing (ICIP 2005)

作者： Zahir, S Naqvi, M UNBC Dept Comp Sci Image Proc Graph & Multimedia Lab Prince George BC Canada

ISBN: (纸本)0780391349

In this paper, we propose a near minimum sparse pattern coding based scheme for binary image compression. Sparse patterns such as those obtained from prediction, image differencing, and other methods can be coded efficiently using the scheme proposed in this paper. In this research we apply our scheme on coordinate representation of rectangular regions via a number of matrices. Such representations allow for efficiently coding these vertices, and hence compress the image significantly. Simulation results show that the proposed scheme outperformed previously published methods for coordinate data coding by nearly 17%. This scheme has low complexity compared with JBIG2.

关键词： binary image compression coordinate data coding block coding sparse patterns

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binary text image compression using overlapping rectangular partitioning

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PATTERN RECOGNITION LETTERS 1999年第1期20卷 81-88页

作者： Quddus, A Fahmy, MM King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

In this paper a new lossless binary text image coding technique based on overlapping partitioning is presented. In this technique, the black regions in the image are first partitioned into a number of overlapping and nonoverlapping rectangles. This partitioning algorithm gives, in general, fewer rectangles than those obtained using nonoverlapping partitioning. After partitioning, the two opposite vertices of each rectangle are compressed using a simple encoding technique. For binary text images (of different languages and fonts) the overlapping partitioning proposed here yields compression ratios better than the nonoverlapping partitioning. In addition, the proposed sche me is suitable for texts consisting of different languages, fonts and sizes. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： image coding digital image compression binary image compression text image compression overlapping rectangular partitioning

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OPTIMAL STRUCTURE OF MEMORY MODELS FOR LOSSLESS compression OF binary image CONTOURS

OPTIMAL STRUCTURE OF MEMORY MODELS FOR LOSSLESS COMPRESSION ...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tabus, Ioan Sarbu, Septimia Tampere Univ Technol Dept Signal Proc FIN-33101 Tampere Finland

ISBN: (纸本)9781457705397

In this paper we study various chain codes, which are representations of binary image contours, in terms of their ability to compress in the best way the contour information using memory models. We consider five chain codes, including the widely used AF8 and 3OT codes, and note that they correspond to memory models of first and second order for contour representation. In order to provide predictive distributions for the arithmetic coding, memory distribution models such as Markov models and context trees utilized in adaptive configurations are used on top of the chain codes. By additionally accounting for all side costs we obtain losslessly decodable files and find the best performer to be the context tree modeling applied to the sequence of 3OT chain codes, surpassing all results recently reported in the literature for the same data set of bilevel images.

关键词： chain codes binary image compression model structure selection context trees lossless compression

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SHAPE ENCODING FOR EDGE MAP image compression

SHAPE ENCODING FOR EDGE MAP IMAGE COMPRESSION

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IEEE International Conference on image Processing (ICIP)

作者： Gerogiannis, Demetrios P. Nikou, Christophoros Kondi, Lisimachos P. Univ Ioannina Dept Comp Sci & Engn GR-45110 Ioannina Greece

ISBN: (纸本)9781479983391

A method for the efficient encoding and decoding of a shape with application to edge map image compression is proposed. The method relies on the modeling of the manifold of a shape by line segments. Then, encoding is performed by collecting the characteristic features of each line segment, namely the starting and ending points and the number of points contributing to the computation of the corresponding segment. The reconstruction of the shape may be obtained by uniform sampling of points from each line segment computed in the encoding process. Experimental analysis demonstrates that in case of employing a robust and efficient line segment detection algorithm, the proposed encoding/decoding scheme exhibits high compression rates, compared to widely used lossless compression methods, while providing low distortion values.

关键词： binary image compression image representation line segments shape encoding shape reconstruction

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Optimal binary vector quantization via enumeration of covering codes

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IEEE TRANSACTIONS ON INFORMATION THEORY 1997年第2期43卷 638-645页

作者： Wu, XL Laboratory of Chromatography DEPg.Fac.Quimica Universidad Nacional Autonoma de Mexico Circuito interior Cd Universitaria/CP 04510 Mexico D.F.Mexico

binary vector quantization (BVQ) refers to block coding of binary vectors under a fidelity measure, Covering codes were studied as a means of lattice BVQ. But a further source coding problem hidden in the equivalence of covering codes has seemingly eluded attention, Given a d-dimensional hypercube (code space), equivalent covering codes of the same covering radius but of different codewords have different expected BVQ distortions for a general probability mass function, Thus one can minimize, within the code equivalence, the expected distortion over all different covering codes, This leads a two-stage optimization scheme for BVQ design. First we use an optimal covering code to minimize the maximum per-vector distortion at a given rate, Then under the minmax constraint, we minimize the expected quantization distortion, This minmax constrained BVQ method (MCBVQ) controls both the maximum and average distortions, and hence improves subjective quality of compressed binary images, MCBVQ also avoids poor local minima that may trap the generalized Lloyd method, The [7, 4] Hamming code and [8, 4] extended Hamming code are found to be particularly suitable for MCBVQ on binary images, An efficient and simple algorithm is introduced to enumerate all distinct [7, 4] Hamming/[8, 4] extended Hamming codes and compute the corresponding expected distortions in optimal MCBVQ design, Furthermore, MCBVQ using linear covering codes has a compact codebook and a fast syndrome-encoding algorithm.

关键词： binary vector quantization covering codes Hamming codes binary image compression distortion measures

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A new binary image representation: Logicodes

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JOURNAL OF VISUAL COMMUNICATION AND image REPRESENTATION 1997年第3期8卷 291-298页

作者： Wu, JG Chung, KL Natl Taiwan Univ Sci Technol Dept Informat Management Taipei 10672 Taiwan Natl Taiwan Univ Sci Technol Grad Program Informat Engn Taipei 10672 Taiwan Natl Taiwan Normal Univ Dept Informat & Comp Educ Taipei 10610 Taiwan

Using bincodes to represent binary images is shown to be very simple and storage-saving. Given a set of bincodes, this paper presents two improved codes, namely, the logicodes and the restricted logicodes to represent binary images. We first transform the given bincodes into a set of logical expressions. Then a minimization technique is employed to reduce the storage space required for these logical expressions, thus obtaining the logicodes, on which set operations can be applied directly. Further, we put some restrictions into these logicodes to make each resulting logicode, called the restricted logicode, representing a connected black block. Given 20 different-type real images, experimental results show that our logicodes (restricted logicodes) present a saving of 29% to 44% (12% to 34%) with respect to bincodes. When compared to Sarkar's method, except spending a little more space, our proposed codes do have three advantages: (1) it is easier to extract the related geometrical coordinates;(2) the bincodes can be used as direct input;i.e., they do compress the bincodes further;and (3) each restricted logicode represents a connected block block. (C) 1997 Academic Press.

关键词： binary image compression Bincodes Logical expressions Logicodes Minimization Sarkar's codes Set operations Spatial data structures

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An Innovative Lossless compression Method for Discrete-Color images

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IEEE TRANSACTIONS ON image PROCESSING 2015年第1期24卷 44-56页

作者： Alzahir, Saif Borici, Arber Univ No British Columbia Dept Comp Sci Prince George BC V2N 4Z9 Canada Univ Victoria Dept Software Engn Victoria BC V8P 5C2 Canada

In this paper, we present an innovative method for lossless compression of discrete-color images, such as map images, graphics, GIS, as well as binary images. This method comprises two main components. The first is a fixed-size codebook encompassing 8 x 8 bit blocks of two-tone data along with their corresponding Huffman codes and their relative probabilities of occurrence. The probabilities were obtained from a very large set of discrete color images which are also used for arithmetic coding. The second component is the row-column reduction coding, which will encode those blocks that are not in the codebook. The proposed method has been successfully applied on two major image categories: 1) images with a predetermined number of discrete colors, such as digital maps, graphs, and GIS images and 2) binary images. The results show that our method compresses images from both categories (discrete color and binary images) with 90% in most case and higher than the JBIG-2 by 5%-20% for binary images, and by 2%-6.3% for discrete color images on average.

关键词： binary image compression color separation discrete-color image compression Huffman coding graph compression

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SHAPE ENCODING FOR EDGE MAP image compression

SHAPE ENCODING FOR EDGE MAP IMAGE COMPRESSION

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IEEE International Conference on image Processing

作者： Demetrios P. Gerogiannis Christophoros Nikou Lisimachos P. Kondi Department of Computer Science and Engineering University of Ioannina

ISBN: (纸本)9781479983407

关键词： binary image compression image representation Line segments Shape encoding Shape reconstruction shape reconfiguration image representation Line sections image compression compression ratio uniform sampling Shape lossless compression telecommunication standards cellular radio

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