Summary form only given. The authors present the design a lossy fractal compression method for silhouette-like bi-level images that has an excellent quality to compression rate ratio. Their approach is based on weighted finite automata (WFA). We reduce the problem of the encoding of a silhouette-like bi-level image to the encoding of two one-variable functions describing the boundary (-ies) of the black and white regions of the given image. One advantage is that the automata encoding different bitplanes can share states.
{"title":"Compression of silhouette-like images based on WFA","authors":"K. Culík, V. Valenta, J. Kari","doi":"10.1109/DCC.1997.582089","DOIUrl":"https://doi.org/10.1109/DCC.1997.582089","url":null,"abstract":"Summary form only given. The authors present the design a lossy fractal compression method for silhouette-like bi-level images that has an excellent quality to compression rate ratio. Their approach is based on weighted finite automata (WFA). We reduce the problem of the encoding of a silhouette-like bi-level image to the encoding of two one-variable functions describing the boundary (-ies) of the black and white regions of the given image. One advantage is that the automata encoding different bitplanes can share states.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114981150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Given a sequence, we consider the maximum number of distinct phrases in any parsing; this definition of complexity is invariant under string reversal. We show that the Lempel-Ziv (1976, 1978) parsings can vary under reversal by a factor on the order of the log of the sequence length. We give two interpretations of maximal parsing, show that they are not equivalent and that one lacks a plausible monotonicity property.
{"title":"On maximal parsings of strings","authors":"H. Helfgott, M. Cohn","doi":"10.1109/DCC.1997.582052","DOIUrl":"https://doi.org/10.1109/DCC.1997.582052","url":null,"abstract":"Given a sequence, we consider the maximum number of distinct phrases in any parsing; this definition of complexity is invariant under string reversal. We show that the Lempel-Ziv (1976, 1978) parsings can vary under reversal by a factor on the order of the log of the sequence length. We give two interpretations of maximal parsing, show that they are not equivalent and that one lacks a plausible monotonicity property.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"410 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124362221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider the problem of allocating bits among pictures in an MPEG video coder to equalize the visual quality of the coded pictures, while meeting buffer and channel constraints imposed by the MPEG video buffering verifier. We address this problem within a framework that consists of three components: (1) a bit production model for the input pictures, (2) a set of bit-rate constraints imposed by the video buffering verifier, and (3) a novel lexicographic criterion for optimality. Under this framework, we derive simple necessary and sufficient conditions for optimality that lead to efficient algorithms.
{"title":"A lexicographic framework for MPEG rate control","authors":"Dzung T. Hoang, Elliot L. Linzer, J. Vitter","doi":"10.1109/DCC.1997.581982","DOIUrl":"https://doi.org/10.1109/DCC.1997.581982","url":null,"abstract":"We consider the problem of allocating bits among pictures in an MPEG video coder to equalize the visual quality of the coded pictures, while meeting buffer and channel constraints imposed by the MPEG video buffering verifier. We address this problem within a framework that consists of three components: (1) a bit production model for the input pictures, (2) a set of bit-rate constraints imposed by the video buffering verifier, and (3) a novel lexicographic criterion for optimality. Under this framework, we derive simple necessary and sufficient conditions for optimality that lead to efficient algorithms.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125526744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given. This paper proposes a new error concealment algorithm for packet video, effectively eliminating error propagation effects. Most standard video codecs use motion compensation to remove temporal redundancy. With such motion compensated interframe processing, any packet loss may generate serious error propagation over more than 10 consecutive frames. This kind of error propagation leads to perceptually annoying artifacts. Thus, proper error concealment algorithms need to be used to reduce this effect. The proposed algorithm adopts a one pixel block overlap coding structure to solve the error propagation problem. If no packet loss occurs, the decoded pixel intensities on the overlap areas should be consistent (with small differences caused by quantization error). When a packet loss occurs, the corresponding reconstructed frame and any frames referring to it are all damaged. Such damage causes inconsistent pixel intensities on the overlap areas of damaged frames. The proposed error concealment method poses the packet loss recovery problem as one of parameter estimation. Lost transform coefficients are estimated by the method of projection onto convex sets (POCS). The estimation is performed in a manner that maximizes the consistency of pixel intensities in the overlap areas of the reconstructed frames. Experimental results (using a modified version of CCITT H.261) show that it can have good error concealment results even when the damaged frame loses all the DCT coefficients.
{"title":"POCS based error concealment for packet video","authors":"G.-S. Yu, M. Marcellin, M.M.-K. Liu","doi":"10.1109/DCC.1997.582151","DOIUrl":"https://doi.org/10.1109/DCC.1997.582151","url":null,"abstract":"Summary form only given. This paper proposes a new error concealment algorithm for packet video, effectively eliminating error propagation effects. Most standard video codecs use motion compensation to remove temporal redundancy. With such motion compensated interframe processing, any packet loss may generate serious error propagation over more than 10 consecutive frames. This kind of error propagation leads to perceptually annoying artifacts. Thus, proper error concealment algorithms need to be used to reduce this effect. The proposed algorithm adopts a one pixel block overlap coding structure to solve the error propagation problem. If no packet loss occurs, the decoded pixel intensities on the overlap areas should be consistent (with small differences caused by quantization error). When a packet loss occurs, the corresponding reconstructed frame and any frames referring to it are all damaged. Such damage causes inconsistent pixel intensities on the overlap areas of damaged frames. The proposed error concealment method poses the packet loss recovery problem as one of parameter estimation. Lost transform coefficients are estimated by the method of projection onto convex sets (POCS). The estimation is performed in a manner that maximizes the consistency of pixel intensities in the overlap areas of the reconstructed frames. Experimental results (using a modified version of CCITT H.261) show that it can have good error concealment results even when the damaged frame loses all the DCT coefficients.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114951724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The problem of constructing models of English text is considered. A number of applications of such models including cryptology, spelling correction and speech recognition are reviewed. The best current models for English text have been the result of research into compression. Not only is this an important application of such models but the amount of compression provides a measure of how well such models perform. Three main classes of models are considered: character based models, word based models, and models which use auxiliary information in the form of parts of speech. These models are compared in terms of their memory usage and compression.
{"title":"Models of English text","authors":"W. Teahan, J. Cleary","doi":"10.1109/DCC.1997.581953","DOIUrl":"https://doi.org/10.1109/DCC.1997.581953","url":null,"abstract":"The problem of constructing models of English text is considered. A number of applications of such models including cryptology, spelling correction and speech recognition are reviewed. The best current models for English text have been the result of research into compression. Not only is this an important application of such models but the amount of compression provides a measure of how well such models perform. Three main classes of models are considered: character based models, word based models, and models which use auxiliary information in the form of parts of speech. These models are compared in terms of their memory usage and compression.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115449008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given. In warping (also known as mesh-based) motion estimation, motion vectors at individual pixels are computed through an interpolation of a subsampled set of motion vectors. A method for calculating optimal warping coefficients was introduced previously. This algorithm finds the interpolation coefficients, at each individual pixel location (within a block), such that the mean squared luminance errors are minimized. It has been observed that optimal coefficients vary widely with time and across different sequences. This observation motivates the optimization of the warping coefficients locally in time. However, doing so requires the encoder to transmit the coefficients to the decoder. Assuming a 16/spl times/16 block and four floating point coefficients per pixel, this would require a considerable overhead in bitrate. Especially in low bitrate regimes, such overhead is likely to be unacceptable. This paper proposes a parametric class of functions to represent the warping interpolation kernels. More specifically, we propose to use the two-parameter family of functions.
{"title":"Parametric warping for motion estimation","authors":"Aria Nosratinia","doi":"10.1109/DCC.1997.582124","DOIUrl":"https://doi.org/10.1109/DCC.1997.582124","url":null,"abstract":"Summary form only given. In warping (also known as mesh-based) motion estimation, motion vectors at individual pixels are computed through an interpolation of a subsampled set of motion vectors. A method for calculating optimal warping coefficients was introduced previously. This algorithm finds the interpolation coefficients, at each individual pixel location (within a block), such that the mean squared luminance errors are minimized. It has been observed that optimal coefficients vary widely with time and across different sequences. This observation motivates the optimization of the warping coefficients locally in time. However, doing so requires the encoder to transmit the coefficients to the decoder. Assuming a 16/spl times/16 block and four floating point coefficients per pixel, this would require a considerable overhead in bitrate. Especially in low bitrate regimes, such overhead is likely to be unacceptable. This paper proposes a parametric class of functions to represent the warping interpolation kernels. More specifically, we propose to use the two-parameter family of functions.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116051110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given. Introduces a new transformation for block-sorting data compression methods. The transformation is similar to the one presented by Burrows and Wheeler, but avoids the drawbacks of uncertain runtime and low performance with large blocks. The cost is a small compression loss and a slower back transformation. In addition to that it is well suited for hardware implementation. Typical applications include real-time data recording, fast communication lines, on the fly compression and any other task requiring high throughput. The difference between this transformation and the original block-sort transformation is that the original transformation sorts on unlimited context, whereas this transformation sorts on limited context (typically a few bytes) and uses the position in the input block to determine the sort order in the case of equal contexts.
{"title":"A fast block-sorting algorithm for lossless data compression","authors":"Dianne M Schindler","doi":"10.1109/DCC.1997.582137","DOIUrl":"https://doi.org/10.1109/DCC.1997.582137","url":null,"abstract":"Summary form only given. Introduces a new transformation for block-sorting data compression methods. The transformation is similar to the one presented by Burrows and Wheeler, but avoids the drawbacks of uncertain runtime and low performance with large blocks. The cost is a small compression loss and a slower back transformation. In addition to that it is well suited for hardware implementation. Typical applications include real-time data recording, fast communication lines, on the fly compression and any other task requiring high throughput. The difference between this transformation and the original block-sort transformation is that the original transformation sorts on unlimited context, whereas this transformation sorts on limited context (typically a few bytes) and uses the position in the input block to determine the sort order in the case of equal contexts.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121973574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multispectral images, such as Thematic Mapper (TM) images, have high spectral correlation among some bands. These bands also have different dynamic ranges. Hence, when linear predictive techniques employed to exploit the spectral and spatial correlation among the bands of a TM image, the variance of the prediction errors becomes greater. Markas and Reif (1993), have used histogram equalization (modification) techniques for lossy compression of multispectral images. In general, histogram equalization techniques are not reversible. However, by defining a monotonically increasing transformation, so that two adjacent gray values will not map to the same gray value of the transformed image, and selecting a target image with a wider probability density function than the source image, one can define a reversible mapping. We introduce a distinct reversible remapping scheme which utilizes sorting permutations. This technique differs from histogram equalization. It is a reversible transformation. We show that, by utilizing the remapping technique introduced and employing linear predictive techniques on a pair of bands, one can achieve better lossless compression than the results reported previously.
{"title":"A remapping technique based on permutations for lossless compression of multispectral images","authors":"Z. Arnavut","doi":"10.1109/DCC.1997.582067","DOIUrl":"https://doi.org/10.1109/DCC.1997.582067","url":null,"abstract":"Multispectral images, such as Thematic Mapper (TM) images, have high spectral correlation among some bands. These bands also have different dynamic ranges. Hence, when linear predictive techniques employed to exploit the spectral and spatial correlation among the bands of a TM image, the variance of the prediction errors becomes greater. Markas and Reif (1993), have used histogram equalization (modification) techniques for lossy compression of multispectral images. In general, histogram equalization techniques are not reversible. However, by defining a monotonically increasing transformation, so that two adjacent gray values will not map to the same gray value of the transformed image, and selecting a target image with a wider probability density function than the source image, one can define a reversible mapping. We introduce a distinct reversible remapping scheme which utilizes sorting permutations. This technique differs from histogram equalization. It is a reversible transformation. We show that, by utilizing the remapping technique introduced and employing linear predictive techniques on a pair of bands, one can achieve better lossless compression than the results reported previously.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"438 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132525783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given. For pt.I see ibid., p.436, 1997. We review prominent examples of adaptive vector quantization (AVQ) algorithms from prior literature and develop a classification of these algorithms. Well known theorems from rate-distortion theory suggest two approaches to the nonadaptive vector quantization (VQ) of a stationary, ergodic random process. These two nonadaptive VQ approaches have, in turn, inspired two general types of AVQ algorithms for the coding of nonstationary sources. In constrained-distortion AVQ algorithms, the algorithm limits the distortion to some maximum value and then attempts to minimize the rate subject to this distortion constraint. Constrained-rate AVQ algorithms do the opposite, limiting the rate to be less than or equal to some maximum value and attempting to produce a coding with the smallest distortion. A third category of AVQ algorithms, rate-distortion-based algorithms minimize the rate-distortion cost functions. We discuss each of the three categories of AVQ algorithms in detail and mention notable algorithms found in each category. Afterwards, we summarize the discussion with an algorithm taxonomy. Finally, we present experimental results for several prominent AVQ algorithms on an artificial nonstationary random process. Our results suggest that, one, the class of rate-distortion-based algorithms is capable of coding performance superior than that of other algorithms, particularly for low-rate coding, and, two, that complex, batch coding algorithms are not as competitive as simpler, online algorithms.
{"title":"Adaptive vector quantization .II. Classification and comparison of algorithms","authors":"J. Fowler","doi":"10.1109/DCC.1997.582095","DOIUrl":"https://doi.org/10.1109/DCC.1997.582095","url":null,"abstract":"Summary form only given. For pt.I see ibid., p.436, 1997. We review prominent examples of adaptive vector quantization (AVQ) algorithms from prior literature and develop a classification of these algorithms. Well known theorems from rate-distortion theory suggest two approaches to the nonadaptive vector quantization (VQ) of a stationary, ergodic random process. These two nonadaptive VQ approaches have, in turn, inspired two general types of AVQ algorithms for the coding of nonstationary sources. In constrained-distortion AVQ algorithms, the algorithm limits the distortion to some maximum value and then attempts to minimize the rate subject to this distortion constraint. Constrained-rate AVQ algorithms do the opposite, limiting the rate to be less than or equal to some maximum value and attempting to produce a coding with the smallest distortion. A third category of AVQ algorithms, rate-distortion-based algorithms minimize the rate-distortion cost functions. We discuss each of the three categories of AVQ algorithms in detail and mention notable algorithms found in each category. Afterwards, we summarize the discussion with an algorithm taxonomy. Finally, we present experimental results for several prominent AVQ algorithms on an artificial nonstationary random process. Our results suggest that, one, the class of rate-distortion-based algorithms is capable of coding performance superior than that of other algorithms, particularly for low-rate coding, and, two, that complex, batch coding algorithms are not as competitive as simpler, online algorithms.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134121440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a technique to compress scalar functions defined on 2-manifolds. Our approach combines discrete wavelet transforms with zerotree compression, building on ideas from three previous developments: the lifting scheme, spherical wavelets, and embedded zerotree coding methods. Applications lie in the efficient storage and rapid transmission of complex data sets. Typical data sets are Earth topography, satellite images, and surface parametrizations. Our contribution is the novel combination and application of these techniques to general 2-manifolds.
{"title":"Compression of functions defined on surfaces of 3D objects","authors":"K. Kolarov, W. Lynch","doi":"10.1109/DCC.1997.582051","DOIUrl":"https://doi.org/10.1109/DCC.1997.582051","url":null,"abstract":"We present a technique to compress scalar functions defined on 2-manifolds. Our approach combines discrete wavelet transforms with zerotree compression, building on ideas from three previous developments: the lifting scheme, spherical wavelets, and embedded zerotree coding methods. Applications lie in the efficient storage and rapid transmission of complex data sets. Typical data sets are Earth topography, satellite images, and surface parametrizations. Our contribution is the novel combination and application of these techniques to general 2-manifolds.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134524296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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