2007 Data Compression Conference (DCC'07)最新文献

{"title":"A Simple Statistical Algorithm for Biological Sequence Compression","authors":"Minh Duc Cao, T. Dix, L. Allison, Chris Mears","doi":"10.1109/DCC.2007.7","DOIUrl":"https://doi.org/10.1109/DCC.2007.7","url":null,"abstract":"This paper introduces a novel algorithm for biological sequence compression that makes use of both statistical properties and repetition within sequences. A panel of experts is maintained to estimate the probability distribution of the next symbol in the sequence to be encoded. Expert probabilities are combined to obtain the final distribution. The resulting information sequence provides insight for further study of the biological sequence. Each symbol is then encoded by arithmetic coding. Experiments show that our algorithm outperforms existing compressors on typical DNA and protein sequence datasets while maintaining a practical running time","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116659008","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}

{"title":"Compression for Low Power Consumption in Battery-powered Handsets","authors":"Mayumi Kato, D. Lo","doi":"10.1109/DCC.2007.24","DOIUrl":"https://doi.org/10.1109/DCC.2007.24","url":null,"abstract":"Only summary form given.Java has been introduced to the mobile/wireless handsets and Java enabled handsets are now prevalent in the market, showing their successful launch. A variety of attractive services have been developed and promise a lot of fun to our daily lives, which are however targeted to the high end products that are very expensive. We focus on economic types with similar performance to the high end products. Memory compression is a key technique to achieve the goal and is integrated into a Java runtime environment. We save memory with runtime compression and reduce power by a memory bank partitioning technique (powering off unused memory banks). In memory compression algorithms and the Java runtime environment are studied. The Wilson and Kaplan (WK) algorithm is discussed as in-memory compression and introduced to the Java heap as an alternative of the garbage collection (gc). Experiments show around 10% of speedup and up to 45% of memory saving in the application core part (20.0 % of memory overhead for a user interface). The Java system using the memory compression outperforms its base configuration and will give further chance to reduce memory demand in combination with the garbage collection designed for the Java compressed heap. Experiments show that the in-heap memory compression technique can save more than 50 % (up to 60 %) of the heap demand and that half of the memory banks for the Java heap may never be turned on. 50 % of the power consumption (memory leakage current) can be saved. The hardware compressor and decompressor are currently ten thousand times faster than the software counterparts. The time overhead could be negligible by introducing the hardware compressor and decompressor. Hardware/software codesign is an additional key to achieve our goal. The contribution of the runtime memory and heap compression is threefold: small memory demand, lower power consumption, and negligible time overhead in battery-powered handsets. Future work includes several issues unsolved in the Java memory compression: studies of integration of CLDC Hot Spot implementation and extension of the Java virtual machine to the traditional virtual machine.","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129221676","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}

{"title":"Joint Optimization of Distributed Broadcast Quantization Systems for Classification","authors":"M. Lexa, Don H. Johnson","doi":"10.1109/dcc.2007.50","DOIUrl":"https://doi.org/10.1109/dcc.2007.50","url":null,"abstract":"We develop a simulated annealing technique to jointly optimize a distributed quantization structure meant to maximize the asymptotic error exponent of a downstream classifier or detector. This distributed structure sequentially processes an input vector and exploits broadcasts to improve the best possible error exponents. The annealing approach is a robust technique that avoids local maxima and is easily tailored to a broadcast quantizer's structural constraints","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"337 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125797394","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}

{"title":"Structure induction by lossless graph compression","authors":"L. Peshkin","doi":"10.1109/DCC.2007.73","DOIUrl":"https://doi.org/10.1109/DCC.2007.73","url":null,"abstract":"This work is motivated by the necessity to automate the discovery of structure in vast and ever-growing collection of relational data commonly represented as graphs, for example genomic networks. A novel algorithm, dubbed Graphitour, for structure induction by lossless graph compression is presented and illustrated by a clear and broadly known case of nested structure in a DNA molecule. This work extends to graphs some well established approaches to grammatical inference previously applied only to strings. The bottom-up graph compression problem is related to the maximum cardinality (non-bipartite) maximum cardinality matching problem. The algorithm accepts a variety of graph types including directed graphs and graphs with labeled nodes and arcs. The resulting structure could be used for representation and classification of graphs","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122536183","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}

{"title":"Lossless Transmission of Correlated Sources over a Multiple Access Channel with Side Information","authors":"Deniz Gündüz, E. Erkip","doi":"10.1109/DCC.2007.53","DOIUrl":"https://doi.org/10.1109/DCC.2007.53","url":null,"abstract":"In this paper, we consider lossless transmission of arbitrarily correlated sources over a multiple access channel. Characterization of the achievable rates in the most general setting is one of the long-standing open problems of information theory. We consider a special case of this problem where the receiver has access to correlated side information given which the sources are independent. We prove a source channel separation theorem for this system, that is, we show that there is no loss in performance in first applying distributed source coding where each encoder compresses its source conditioned on the side information at the receiver, and then applying an optimal multiple access channel code with independent codebooks. We also give necessary and sufficient conditions for source and channel separability in the above problem if there is a perfect two-sided feedback from the receiver to the transmitters. These two communication scenarios constitute examples of few non-trivial multi-user scenarios for which separation holds","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126465139","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}

{"title":"Practical Binary Adaptive Block Coder","authors":"Y. Reznik","doi":"10.1109/DCC.2007.66","DOIUrl":"https://doi.org/10.1109/DCC.2007.66","url":null,"abstract":"We design a low-complexity algorithm for adaptive encoding/decoding of binary sequences produced by memoryless sources. Our algorithm processes input data in blocks of n = 8..16 bits, and uses numbers of nonzero bits in previously encoded blocks as contexts","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130089716","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}

{"title":"Generalized Statistics Framework for Rate Distortion Theory","authors":"R. C. Venkatesan, Angelo Plastino","doi":"10.1109/DCC.2007.41","DOIUrl":"https://doi.org/10.1109/DCC.2007.41","url":null,"abstract":"A statistical physics analysis for the variational problem encountered in rate distortion (RD) theory and the information bottleneck (IB) method, derived within the generalized nonextensive statistics framework of Tsallis, is presented. The governing equations of the alternate minimization algorithm for the generalized Bergman RD (GBRD) model are described.","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"12 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128847194","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}

{"title":"Simple Linear-Time Off-Line Text Compression by Longest-First Substitution","authors":"Ryosuke Nakamura, H. Bannai, Shunsuke Inenaga, M. Takeda","doi":"10.1109/DCC.2007.70","DOIUrl":"https://doi.org/10.1109/DCC.2007.70","url":null,"abstract":"We consider grammar based text compression with longest-first substitution, where non-overlapping occurrences of a longest repeating substring of the input text are replaced by a new non-terminal symbol. We present a new text compression algorithm by simplifying the algorithm presented in S. Inenaga et al., (2003). We give a new formulation of the correctness proof introducing the sparse lazy suffix tree data structure. We also present another type of longest-first substitution strategy that allows better compression. We show results of preliminary experiments comparing grammar sizes of the two versions of the longest-first strategy and the most frequent strategy","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129601350","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}