G.G. Langdon and J. Rissanen, "Compression of black-white images with arithmetic coding", IEEE Trans. Communications, 29 (6), 858-867, June 1981.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....bit planes. It generates the input to the BAC block based on statistics (state information bits that are maintained across the bit planes) of the data coded previously. BAC: The BPC outputs are entropy coded using BAC to generate the code stream. The MQ coder, which is a derivative of the coder [11], 12] has been proposed to implement the BAC. The algorithm is multiplication free. Predetermined probability values are supplied by the standard and are stored in a look up table. The adaptation state machine is also supplied by the standard. File formatting and layer formation: For each of ....

G. L. Langdon, Jr. and J. Rissanen, "Compression of black-white images with arithmetic coding," IEEE Trans. Commun., vol. COM-29, pp. 858--867, June 1981.

....data compression, where the two mafn approaches are [4] are modebased (e.g. Arithmetic Coding (AC) or adaptive Huffman coding) and dictionary based (e.g. Lempel Ziv (LZ) coding) where the adaptivity comes from dynamically updating, respectively, the model and the dictionary. In the AC algorithm [1], in the simpler case of a binary source, the encoder has to update the probabilities of the 0 s and 1 s. If the source is stationary and the model is correct then AC can provide a performance very close to the first order entropy. However, in real life environments, where sources need not be ....

G. G. Langdon and J. Rissancn, "Compression of black-white images with arithmetic coding," IEEE Trans. on Comm., vol. COM-29, pp. 858 867, Jun. 1981.

....the feature image. The method is near lossless because the amount of changes is controlled only isolated noise pixels are reversed. 2. Context based compression Binary images are favorable source for context based image compression because of the spatial dependencies between neighboring pixels [7,8]. In context based compression, the pixels are coded on the basis of their probability estimates in respect to the context. The context is deftned by the combination of the color values of already coded neighboring pixels within the template. JBIG is the current international standard for ....

Langdon G.G., Rissanen J., Compression of blackwhite images with arithmetic coding. IEEE Trans. Communications 29 (6): 858-867, 1981.

....discussions of a particular arithmetic code for an arbitrary source having an alphabet of K letters. This code is referred to as a K ar t arithmetic code, to distinguish it from the more widely used (and perhaps more widely understood) arithmetic codes designed for binary source alphabets [32] [47] [48] Although ultimately the code is to be applied to the scalar quantization problem for a memoryless source, the discussion in these first sections does not assume independence of source letters; this is to make the material useful to those interested in high performance entropy coding in the ....

....Thus, carry trapping must be disabled as long as the carry control field contains a carry trap bit. This consideration leads to the two state carry trapping mechanism as indicated in Figure 3.19. This method of controlling the propagation of carry overs was devised by Langdon and Rissanen [47], and may have been based on a similar technique devised by Rubin [60] 3.6 Performance of Arithmetic Coding Applied to Quantization 75 rate distortion function uniform quantization with ideal entropy coding uniform quantization with arithmetic coding uniform quantization with ideal ....

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Langdon, G., and Rissanen, J.J., "Compression of Black-White Images with Arithmetic Coding," IEEE Trans. Commun., vol. COM-29, no. 6, pp. 858-867, June 1981.

....and the PMF to be specified independently for each pixel encoded. The PMF can be conditioned on anything that the decoder will have access to prior to the time of decoding. One of the first published applications of arithmetic coding was a scheme for lossless compres sion of binary images [69], which is depicted in Figure 4.1.1. Pixels (which can be either black or white) are scanned in raster order and fed to an arithmetic coder, along with a corresponding estimate of the conditional PMF. The arithmetic coder then produces a compact sequence of bits from which the original image may ....

....the size of the alphabet of x for any of the neighborhoods shown in Figure 4.1.2 is small enough that it is feasible to estimate the conditional PMF for each x by counting x specific occurrences of the values of y. Several variations of such a count based estimation procedure are described in [69]. Count based PMF estimation is not appropriate for greyscale images, because the number of conditioning states becomes prohibitively large for even moderate size neighborhoods. Also, that method of estimation when applied to scalar observations does not exploit the relationship between nearby ....

Glen G. Langdon and Jorma J. Rissanen. Compression of black-white images with arithmetic coding. IEEE Trans. Comm., COM-29:858-867, June 1981.

....some novel methods for choosing the optimal resolution order combination without an exhaustive search or even a pre scan of the data. As a final introductory note, the algorithms described in this paper are adaptive. Adaptive Markov models, most often used in conjunction with arithmetic coding [3], start with no information about the source, accept data sequentially, are presented with each symbol in the sample only once, and modify the way they compress in response to the history. Adaptive algorithms are attractive in that it is unnecessary to perform a pre scan of the data, and no side ....

....in that it is unnecessary to perform a pre scan of the data, and no side information need be sent. 2. PRELIMINARIES Before proceeding, we need to introduce the terms state weight, which is simply the number of contexts in a Markov modeller and permutation, which is an ordered set of integers [3], p = fp 1 #p 2 # Delta Delta Delta#p ng, where n is the model order. The source to be coded is given by x t = x 1 #x 2 # Delta Delta Delta#x t , where x i is the i th symbol, and we define the order n context of x at symbol t using permutation p as the sequence x t;p 1 #x t;p 2 # ....

G. Langdon and J. Rissanen, "Compression of black-white images with arithmetic coding," IEEE Transactions on Communications, vol. 29, pp. 858--867, 1981.

....are usually the vast majority) to zero. The data stream then consists of two parts: the transmission of a sparse binary map indicating the locations of the non zero pixels (coded using either run length encoding [2] or arithmetic coding [4] followed by the quantized values. Arithmetic coding [10] is more powerful than runlength coding and gives better results at the expense of greater computation. The basic idea behind binary arithmetic coding is to use a finite state machine, whose state is determined by previously coded pixels, to estimate the probability that the next pixel will be a ....

....g ij (m# n) f ij (m# n) f ij (m# n) i# j 2f(0# 1)# (1# 0)# (1# 1)g# (6) which can then be coded instead of the original subbands. The second method is based on the arithmetic coding algo (a) Lena (256 Theta 256) b) Barb (512 Theta 512) Fig. 1. The Original Images rithm given in [10] and uses the predicted values to guess at which of the highpass pixels will be non zero after deadzone quantization. If the prediction is fairly good, then the guess will improve the prediction as to whether or not the next pixel will be a 1 , allowing the binary map to be coded at a lower ....

G. G. Langdon and J. Rissanen, "Compression of black-white images with arithmetic coding," IEEE Trans. Commun.,vol. 29, pp. 858--867, June 1981.

....be transparent for data superimposing, and 1 stands for the foreground color. Binary images can be efficiently compressed using context based statistical modeling and arithmetic coding. The image is processed pixel by pixel in raster scan order starting from the top leftmost pixel of the image [15]. The probabilities of the black and white pixels are conditioned on the combination of already coded neighboring pixels defined by a template, see Fig. 1. The above approach is adopted in the international standard JBIG [11,12] and the emerging standard JBIG2 [13,14] Furthermore, JBIG2 segments ....

G.G. Langdon, J. Rissanen, "Compression of black-white images with arithmetic coding", IEEE Trans. Communications, 29 (6), 858-867, June 1981.

....that improved performance can be obtained by using a variable decay rate scheme that uses the derivative of the per symbol codelength sequence to control the rate of decay. 1. INTRODUCTION Consider the task of compressing a non stationary memoryless source. We know that arithmetic coding [1][2] can compress the source to a rate corresponding to the entropy of the probability model of the source, so that determining the probability model is the central problem. If we constrain ourselves to adaptive algorithms that process data sequentially, are presented with each symbol in the sample ....

G. Langdon and J. Rissanen, "Compression of blackwhite images with arithmetic coding," IEEE Transactions on Communications, vol. 29, pp. 858--867, 1981.

....allows the application of binary adaptation to M ary alphabets. Decomposition makes binary alphabets and adaptation attractive because Shannon s notion of entropy provides that the binary decomposition of any M ary alphabet does not change the entropy value. Early binary adaptation techniques [8, 7], were developed as universal parts for building and then combining with a coder and a context model designed for each particular application. Other adapters appear in [12, 11, 10] With binary adapters, a multiple outcome event is transformed to a decision tree of binary events. Each leaf of the ....

....SSS adap constrains the LPS count to values 3, 4, and 5. When the LPS count reaches 6, the LPS count is halved to 3, and the total count is halved as well. The algorithm LPS3:5;1 is so called because the LPS count range is 3 to 5, and the count increment is 1. This algorithm is described in [8]. With value LPSct as the current count for the LPS symbol, and TOTct as the total count, their current count ratio is the value pLPS, the probability of the less probable symbol. The probability estimate pLPS is: pLPS = LPSct TOT ct : We describe LPS3:5;1 by means of a program snippit. We ....

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G. G. Langdon, Jr. and J. J. Rissanen. \Compression of black-white images with arithmetic coding ". IEEE Trans. Commun., vol. COM-29, no. 6:pp. 858-867, June. 1981.

....remaining constraint is that the statistical model may be conditioned only on preceding pixels in the chosen ordering. The conditioning structure of the statistical model we consider is patterned after the grayscale extension [10] of the causal neighborhood context model originally proposed in [7] for binary images. Speci cally, for every pixel location in the sequence, a set of nearby but strictly preceding pixel locations is speci ed as a conditioning context. We consider the simplest case, wherein the pixels are encoded in raster order and the set of conditioning pixels is speci ed ....

G. G. Langdon and J. J. Rissanen. Compression of black-white images with arithmetic coding. IEEE Trans. Comm., COM-29:858-867, June 1981.

....probabilities directly in the coding process, or with some multiplication and division, use the counts value themselves directly in the coding process. There are many ways to incorporate the count ratio and scaled count techniques into the arithmetic code. Perhaps the first such is described in [lr81], where adapter SSS adap provides the coding parameter needed by the shift coder also described in [lr81] Several techniques are reported in the IBM Technical Disclosure Bulletin. For mary (m symbol) alphabets, and arithmetic adapter coder is described by Goertzel in File Compressor [goe87] ....

....themselves directly in the coding process. There are many ways to incorporate the count ratio and scaled count techniques into the arithmetic code. Perhaps the first such is described in [lr81] where adapter SSS adap provides the coding parameter needed by the shift coder also described in [lr81]. Several techniques are reported in the IBM Technical Disclosure Bulletin. For mary (m symbol) alphabets, and arithmetic adapter coder is described by Goertzel in File Compressor [goe87] The standard distributions employed by statisticians and others, such as the Poisson, Gaussian, or ....

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G. Langdon and J. Rissanen, "Compression of Black-white images with arithmetic coding", IEEE Trans commm., vol COM-29, no 6, 858-867, June 1981.

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G.G. Langdon and J. Rissanen, "Compression of black-white images with arithmetic coding", IEEE Trans. Communications, 29 (6), 858-867, June 1981.

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G.G. Langdon, J. Rissanen, Compression of black -- white images with arithmetic coding, IEEE Transactions on Communications 29 (6) (1981) 858 -- 867. June.

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Langdon G.G., Rissanen J., Compression of blackwhite images with arithmetic coding. IEEE Trans. Communications 29 (6): 858-867, 1981.

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Langdon GG, Rissanen J (1981) "Compression of black-white images with arithmetic coding", IEEE Trans. Communications 29: 858-867.

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G.G. Langdon, J. Rissanen, "Compression of black-white images with arithmetic coding", IEEE Trans. Communications, 29 (6), 858-867, June 1981.

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G. G. Langdon and J. Rissanen, "Compression of black-white images with arithmetic coding," IEEE Trans. Commun. 29#6#, 858--867 #1981#.

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G. Langdon Jr and J. Rissanen. Compression of black--white images with arithmetic coding. IEEE Transactions on Communications, 29(6):858--867, 1981.

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Langdon G.G., Rissanen J., Compression of blackwhite images with arithmetic coding. IEEE Trans. Communications 29 (6): 858-867, 1981.

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G. G. Langdon and J. Rissanen, "Compression of black-white images with arithmetic coding," IEEE Trans. Commun., vol. COMM-29, pp. 858--867, June 1981.

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G.G. Langdon Jr. and J.J. Rissanen, "Compression of black-white images with arithmetic coding," IEEE Trans. Communications, vol. 29(6), pp. 858--867, June 1981. 21

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G. Langdon and J. Rissanen, "Compression of black-white images with arithmetic coding," IEEE Trans. Commun., vol. COM-6, pp. 158--167, 1981.

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G. G. Langdon and J. Rissanen, "Compression of black-white images with arithmetic coding," IEEE Trans. Commun. 29#6#, 858--867 #1981#.

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Glen G. Langdon, Jr. and Jorma Rissanen, \Compression of Black-White Images with Arithmetic Coding ", IEEE Trans. Communications, vol COM-29, No. 6, June 1981, 858-867.

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