Summary form only given. It is necessary to develop a quality measure that is capable of determining (1) the amount of degradation, (2) the type of degradation, and (3) the impact of compression on different frequency ranges, in a reconstructed image. We discuss the development of a new graphical measure based on three criteria. To be able to make a local error analysis, we first divide a given image (the original or a degraded) into areas with certain activity levels using, as in the case of Hosaka plots, a quadtree decomposition. The largest and smallest block sizes in our decomposition scheme are 16 and 2, respectively. This gives us 4 classes of blocks having the same size. Class i represents the collection of i/spl times/i blocks; a higher value of i denotes a lower frequency area of the image. After obtaining the quadtree decomposition for a specified value of the variance threshold, we compute three values for each class i (i=2,4,8,16), and normalize them according to: (1) the number of pixels/the number of pixels in the entire image; (2) the number of distinct pixel values/the number of possible pixel values; and (3) the average of the standard deviations in the blocks/a preset maximum standard deviation. The essential characteristics of the image are then displayed in a normalized bar chart. This lays the foundations for designing optimized image coders.
{"title":"A multi-dimensional measure for image quality","authors":"A. Eskicioglu","doi":"10.1109/DCC.1995.515579","DOIUrl":"https://doi.org/10.1109/DCC.1995.515579","url":null,"abstract":"Summary form only given. It is necessary to develop a quality measure that is capable of determining (1) the amount of degradation, (2) the type of degradation, and (3) the impact of compression on different frequency ranges, in a reconstructed image. We discuss the development of a new graphical measure based on three criteria. To be able to make a local error analysis, we first divide a given image (the original or a degraded) into areas with certain activity levels using, as in the case of Hosaka plots, a quadtree decomposition. The largest and smallest block sizes in our decomposition scheme are 16 and 2, respectively. This gives us 4 classes of blocks having the same size. Class i represents the collection of i/spl times/i blocks; a higher value of i denotes a lower frequency area of the image. After obtaining the quadtree decomposition for a specified value of the variance threshold, we compute three values for each class i (i=2,4,8,16), and normalize them according to: (1) the number of pixels/the number of pixels in the entire image; (2) the number of distinct pixel values/the number of possible pixel values; and (3) the average of the standard deviations in the blocks/a preset maximum standard deviation. The essential characteristics of the image are then displayed in a normalized bar chart. This lays the foundations for designing optimized image coders.","PeriodicalId":107017,"journal":{"name":"Proceedings DCC '95 Data Compression Conference","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133608207","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 method that represents a signal with respect to an overcomplete set of vectors which we call a dictionary. The use of overcomplete sets of vectors (redundant bases or frames) together with quantization is explored as an alternative to transform coding for signal compression. The goal is to retain the computational simplicity of transform coding while adding flexibility like adaptation to signal statistics. We show results using both fixed quantization in frames and greedy quantization using matching pursuit. An MSE slope of -6 dB/octave of frame redundancy is shown for a particular tight frame and is verified experimentally for another frame.
{"title":"Quantization of overcomplete expansions","authors":"Vivek K Goyal, M. Vetterli, N. T. Thao","doi":"10.1109/DCC.1995.515491","DOIUrl":"https://doi.org/10.1109/DCC.1995.515491","url":null,"abstract":"We present a method that represents a signal with respect to an overcomplete set of vectors which we call a dictionary. The use of overcomplete sets of vectors (redundant bases or frames) together with quantization is explored as an alternative to transform coding for signal compression. The goal is to retain the computational simplicity of transform coding while adding flexibility like adaptation to signal statistics. We show results using both fixed quantization in frames and greedy quantization using matching pursuit. An MSE slope of -6 dB/octave of frame redundancy is shown for a particular tight frame and is verified experimentally for another frame.","PeriodicalId":107017,"journal":{"name":"Proceedings DCC '95 Data Compression Conference","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115067141","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}
Current approaches to speech and handwriting recognition demand a strong language model with a small number of states and an even smaller number of parameters. We introduce four new techniques for statistical language models: multicontextual modeling, nonmonotonic contexts, implicit context growth, and the divergence heuristic. Together these techniques result in language models that have few states, even fewer parameters, and low message entropies. For example, our techniques achieve a message entropy of 2.16 bits/char on the Brown corpus using only 19374 contexts and 54621 parameters. Multicontextual modeling and nonmonotonic contexts, are generalizations of the traditional context model. Implicit context growth ensures that the state transition probabilities of a variable-length Markov process are estimated accurately. This technique is generally applicable to any variable-length Markov process whose state transition probabilities are estimated from string frequencies. In our case, each state in the Markov process represents a context, and implicit context growth conditions the shorter contexts on the fact that the longer contexts did not occur. In a traditional unicontext model, this technique reduces the message entropy of typical English text by 0.1 bits/char. The divergence heuristic, is a heuristic estimation algorithm based on Rissanen's (1978, 1983) minimum description length (MDL) principle and universal data compression algorithm.
{"title":"Context models in the MDL framework","authors":"E. Ristad, Robert G. Thomas","doi":"10.1109/DCC.1995.515496","DOIUrl":"https://doi.org/10.1109/DCC.1995.515496","url":null,"abstract":"Current approaches to speech and handwriting recognition demand a strong language model with a small number of states and an even smaller number of parameters. We introduce four new techniques for statistical language models: multicontextual modeling, nonmonotonic contexts, implicit context growth, and the divergence heuristic. Together these techniques result in language models that have few states, even fewer parameters, and low message entropies. For example, our techniques achieve a message entropy of 2.16 bits/char on the Brown corpus using only 19374 contexts and 54621 parameters. Multicontextual modeling and nonmonotonic contexts, are generalizations of the traditional context model. Implicit context growth ensures that the state transition probabilities of a variable-length Markov process are estimated accurately. This technique is generally applicable to any variable-length Markov process whose state transition probabilities are estimated from string frequencies. In our case, each state in the Markov process represents a context, and implicit context growth conditions the shorter contexts on the fact that the longer contexts did not occur. In a traditional unicontext model, this technique reduces the message entropy of typical English text by 0.1 bits/char. The divergence heuristic, is a heuristic estimation algorithm based on Rissanen's (1978, 1983) minimum description length (MDL) principle and universal data compression algorithm.","PeriodicalId":107017,"journal":{"name":"Proceedings DCC '95 Data Compression Conference","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129747861","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. Binary variable order adaptive algorithms like the UMC of Rissanen (1986) and JBIG can be used to losslessly compress non-binary data by splitting the data into planes, each of 1 bit resolution, and passing each plane to a separate instance of the algorithm. The UMC algorithm operated in this way is the most powerful lossless signal data compressor the authors are aware of. We attempt to develop an understanding of why this approach is so effective. We investigate the common technique of Gray coding the data before splitting it into single-bit planes and passing to the modeler and coder, and compare it to a simple weighted binary coding. We then propose a non-binary pseudo-Gray code as a method of generating planes of resolution greater than or equal to 1 bit, and compare it with the other conventional methods. The algorithm to generate the pseudo-Gray code is much the same as that for the construction of a binary Gray code, except that instead of minimizing the Hamming distance between neighboring bit planes, we instead minimize the Euclidean distance between adjacent groups of bit planes.
{"title":"Bitgroup modeling of signal data for image compression","authors":"J. Vaisey, Mark Trumbo","doi":"10.1109/DCC.1995.515576","DOIUrl":"https://doi.org/10.1109/DCC.1995.515576","url":null,"abstract":"Summary form only given. Binary variable order adaptive algorithms like the UMC of Rissanen (1986) and JBIG can be used to losslessly compress non-binary data by splitting the data into planes, each of 1 bit resolution, and passing each plane to a separate instance of the algorithm. The UMC algorithm operated in this way is the most powerful lossless signal data compressor the authors are aware of. We attempt to develop an understanding of why this approach is so effective. We investigate the common technique of Gray coding the data before splitting it into single-bit planes and passing to the modeler and coder, and compare it to a simple weighted binary coding. We then propose a non-binary pseudo-Gray code as a method of generating planes of resolution greater than or equal to 1 bit, and compare it with the other conventional methods. The algorithm to generate the pseudo-Gray code is much the same as that for the construction of a binary Gray code, except that instead of minimizing the Hamming distance between neighboring bit planes, we instead minimize the Euclidean distance between adjacent groups of bit planes.","PeriodicalId":107017,"journal":{"name":"Proceedings DCC '95 Data Compression Conference","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130010762","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. The most popular adaptive dictionary coding scheme used for text compression is the LZW algorithm. In the LZW algorithm, a changing dictionary contains common strings that have been encountered so far in the text. The dictionary can be represented by a dynamic trie. The input text is examined character by character and the longest substring (called a prefix string) of the text which already exists in the trie, is replaced by a pointer to a node in the trie which represents the prefix string. Motivation of our research is to explore a variation of the LZW algorithm for variable-length binary encoding of text (we call it the LZWA algorithm) and to develop a memory-based VLSI architecture for text compression. We proposed a new methodology to represent the trie in the form of a binary tree (we call it a binary trie) to maintain the dictionary used in the LZW scheme. This binary tree maintains all the properties of the trie and can easily be mapped into memory. As a result, the common substrings can be encoded using variable length prefix binary codes. The prefix codes enable us to uniquely decode the text in its original form. The algorithm outperforms the usual LZW scheme when the size of the text is small (usually less than 5 K). Depending upon the characteristics of the text, the improvement of the compression ratio has been achieved around 10-30% compared to the LZW scheme. But its performance degrades for larger size texts.
{"title":"A tree based binary encoding of text using LZW algorithm","authors":"T. Acharya, A. Mukherjee","doi":"10.1109/DCC.1995.515573","DOIUrl":"https://doi.org/10.1109/DCC.1995.515573","url":null,"abstract":"Summary form only given. The most popular adaptive dictionary coding scheme used for text compression is the LZW algorithm. In the LZW algorithm, a changing dictionary contains common strings that have been encountered so far in the text. The dictionary can be represented by a dynamic trie. The input text is examined character by character and the longest substring (called a prefix string) of the text which already exists in the trie, is replaced by a pointer to a node in the trie which represents the prefix string. Motivation of our research is to explore a variation of the LZW algorithm for variable-length binary encoding of text (we call it the LZWA algorithm) and to develop a memory-based VLSI architecture for text compression. We proposed a new methodology to represent the trie in the form of a binary tree (we call it a binary trie) to maintain the dictionary used in the LZW scheme. This binary tree maintains all the properties of the trie and can easily be mapped into memory. As a result, the common substrings can be encoded using variable length prefix binary codes. The prefix codes enable us to uniquely decode the text in its original form. The algorithm outperforms the usual LZW scheme when the size of the text is small (usually less than 5 K). Depending upon the characteristics of the text, the improvement of the compression ratio has been achieved around 10-30% compared to the LZW scheme. But its performance degrades for larger size texts.","PeriodicalId":107017,"journal":{"name":"Proceedings DCC '95 Data Compression Conference","volume":"160 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128972985","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}
J. Kanai, S. Latifi, G. Rajarathinam, G. Nagy, H. Bunke
A formal framework of directly processing the encoded data is presented. Image operations which can be directly and efficiently applied on run-length encoded data are identified. The FSM and attributed FSM models are used to describe these operations.
{"title":"Operations on compressed image data","authors":"J. Kanai, S. Latifi, G. Rajarathinam, G. Nagy, H. Bunke","doi":"10.1109/DCC.1995.515542","DOIUrl":"https://doi.org/10.1109/DCC.1995.515542","url":null,"abstract":"A formal framework of directly processing the encoded data is presented. Image operations which can be directly and efficiently applied on run-length encoded data are identified. The FSM and attributed FSM models are used to describe these operations.","PeriodicalId":107017,"journal":{"name":"Proceedings DCC '95 Data Compression Conference","volume":"23 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125686828","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. Vector quantisation (VQ) may be adapted for lossless data compression if the data exhibit vector structures, such as in textural relational databases. Lossless VQ is discussed and it is demonstrated that a relation of tuples may be encoded and allocated to physical disk blocks such that standard database operations such as access, insertion, deletion, and update may be fully supported.
{"title":"Vector quantization for lossless textual data compression","authors":"W. K. Ng, C. Ravishankar","doi":"10.1109/DCC.1995.515584","DOIUrl":"https://doi.org/10.1109/DCC.1995.515584","url":null,"abstract":"Summary form only given. Vector quantisation (VQ) may be adapted for lossless data compression if the data exhibit vector structures, such as in textural relational databases. Lossless VQ is discussed and it is demonstrated that a relation of tuples may be encoded and allocated to physical disk blocks such that standard database operations such as access, insertion, deletion, and update may be fully supported.","PeriodicalId":107017,"journal":{"name":"Proceedings DCC '95 Data Compression Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130761452","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. Data in volume form consumes an extraordinary amount of storage space. For efficient storage and transmission of such data, compression algorithms are imperative. However, most volumetric data sets are used in biomedicine and other scientific applications where lossy compression is unacceptable. We present a lossless data compression algorithm which uses optimal linear prediction to exploit correlations in all three dimensions. Our algorithm is a combination of differential pulse-code modulation (DPCM) and Huffman coding and results in compression of around 50% for a set of volume data files. The compression algorithm was run with each of the different predictors on a set of volumes consisting of MRI images, CT images, and electron-density map data.
{"title":"Optimal linear prediction for the lossless compression of volume data","authors":"J. Fowler, R. Yagel","doi":"10.1109/DCC.1995.515568","DOIUrl":"https://doi.org/10.1109/DCC.1995.515568","url":null,"abstract":"Summary form only given. Data in volume form consumes an extraordinary amount of storage space. For efficient storage and transmission of such data, compression algorithms are imperative. However, most volumetric data sets are used in biomedicine and other scientific applications where lossy compression is unacceptable. We present a lossless data compression algorithm which uses optimal linear prediction to exploit correlations in all three dimensions. Our algorithm is a combination of differential pulse-code modulation (DPCM) and Huffman coding and results in compression of around 50% for a set of volume data files. The compression algorithm was run with each of the different predictors on a set of volumes consisting of MRI images, CT images, and electron-density map data.","PeriodicalId":107017,"journal":{"name":"Proceedings DCC '95 Data Compression Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129264430","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. A fundamental problem in the construction of statistical techniques for data compression of sequential text is the generation of probabilities from counts of previous occurrences. Each context used in the statistical model accumulates counts of the number of times each symbol has occurred in that context. So in a binary alphabet there will be two counts C/sub 0/ and C/sub 1/ (the number of times a 0 or 1 has occurred). The problem then is to take the counts and generate from them a probability that the next character will be a 0 or 1. A naive estimate of the probability of character i could be obtained by the ratio p/sub i/=C/sub i//(C/sub 0/+C/sub i/). A fundamental problem with this is that it will generate a zero probability if C/sub 0/ or C/sub 1/ is zero. Unfortunately, a zero probability prevents coding from working correctly as the "optimum" code length in this case is infinite. Consequently any estimate of the probabilities must be non-zero even in the presence of zero counts. This problem is called the zero frequency problem . A well known solution to the problem was formulated by Laplace and is known as Laplace's law of succession. We have investigated the correctness of Laplace's law by experiment.
{"title":"Experiments on the zero frequency problem","authors":"J. Cleary, W. Teahan","doi":"10.1109/DCC.1995.515590","DOIUrl":"https://doi.org/10.1109/DCC.1995.515590","url":null,"abstract":"Summary form only given. A fundamental problem in the construction of statistical techniques for data compression of sequential text is the generation of probabilities from counts of previous occurrences. Each context used in the statistical model accumulates counts of the number of times each symbol has occurred in that context. So in a binary alphabet there will be two counts C/sub 0/ and C/sub 1/ (the number of times a 0 or 1 has occurred). The problem then is to take the counts and generate from them a probability that the next character will be a 0 or 1. A naive estimate of the probability of character i could be obtained by the ratio p/sub i/=C/sub i//(C/sub 0/+C/sub i/). A fundamental problem with this is that it will generate a zero probability if C/sub 0/ or C/sub 1/ is zero. Unfortunately, a zero probability prevents coding from working correctly as the \"optimum\" code length in this case is infinite. Consequently any estimate of the probabilities must be non-zero even in the presence of zero counts. This problem is called the zero frequency problem . A well known solution to the problem was formulated by Laplace and is known as Laplace's law of succession. We have investigated the correctness of Laplace's law by experiment.","PeriodicalId":107017,"journal":{"name":"Proceedings DCC '95 Data Compression Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125588622","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. The performance of a quantizer depends primarily on the selection of a codebook. Most of the quantization techniques used in the past are based on a static codebook which stays unchanged for the entire input. As already demonstrated successfully in lossless data compression, adaptation can be very beneficial in the compression of typically changing input data. Adaptive quantization has been difficult to accomplish because of its lossy nature. We present a model for distribution-free adaptive image quantization based on learning classifier systems which have been used successfully in machine learning. A basic learning classifier system is a special type of message-processing, rule-based system that produces output according to its input environment. Probabilistic learning mechanisms are used to dynamically direct the behavior of the system to adapt to its environment. The adaptiveness of a learning classifier system seems very appropriate for the quantization problem. A learning classifier system based adaptive quantizer consists of the input data, a codebook, and the output. When an input can not be matched, a new codebook entry is constructed to match the input. Such an algorithm allows us not only to deal with the changing environment, but also to control the quality of the quantized output. The adaptive quantizers presented can be applied to both scalar quantization and vector quantization. Experimental results for each case in image quantization are very promising.
{"title":"Adaptive image quantization based on learning classifier systems","authors":"Jianhua Lin","doi":"10.1109/DCC.1995.515587","DOIUrl":"https://doi.org/10.1109/DCC.1995.515587","url":null,"abstract":"Summary form only given. The performance of a quantizer depends primarily on the selection of a codebook. Most of the quantization techniques used in the past are based on a static codebook which stays unchanged for the entire input. As already demonstrated successfully in lossless data compression, adaptation can be very beneficial in the compression of typically changing input data. Adaptive quantization has been difficult to accomplish because of its lossy nature. We present a model for distribution-free adaptive image quantization based on learning classifier systems which have been used successfully in machine learning. A basic learning classifier system is a special type of message-processing, rule-based system that produces output according to its input environment. Probabilistic learning mechanisms are used to dynamically direct the behavior of the system to adapt to its environment. The adaptiveness of a learning classifier system seems very appropriate for the quantization problem. A learning classifier system based adaptive quantizer consists of the input data, a codebook, and the output. When an input can not be matched, a new codebook entry is constructed to match the input. Such an algorithm allows us not only to deal with the changing environment, but also to control the quality of the quantized output. The adaptive quantizers presented can be applied to both scalar quantization and vector quantization. Experimental results for each case in image quantization are very promising.","PeriodicalId":107017,"journal":{"name":"Proceedings DCC '95 Data Compression Conference","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123829818","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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