Shuang Wang, Xiaoqian Jiang, Lijuan Cui, Wenrui Dai, N. Deligiannis, Pinghao Li, H. Xiong, Samuel Cheng, L. Ohno-Machado
In this paper, we develop a novel genome compression framework based on distributed source coding (DSC)[3], which is specially tailored to the need of miniaturized devices. At the encoder side, subsequences with adaptive code length can be compressed flexibly through either low complexity DSC based syndrome coding or hash coding with the decision determined by the existence of variations between source and reference known from the decoder feedback. Moreover, to tackle the variations between source and reference at the decoder, we carefully designed a factor graph based low-density parity-check (LDPC) decoder, which automatically detects insertion, deletion and substitution.
{"title":"Genome Sequence Compression with Distributed Source Coding","authors":"Shuang Wang, Xiaoqian Jiang, Lijuan Cui, Wenrui Dai, N. Deligiannis, Pinghao Li, H. Xiong, Samuel Cheng, L. Ohno-Machado","doi":"10.1109/DCC.2013.104","DOIUrl":"https://doi.org/10.1109/DCC.2013.104","url":null,"abstract":"In this paper, we develop a novel genome compression framework based on distributed source coding (DSC)[3], which is specially tailored to the need of miniaturized devices. At the encoder side, subsequences with adaptive code length can be compressed flexibly through either low complexity DSC based syndrome coding or hash coding with the decision determined by the existence of variations between source and reference known from the decoder feedback. Moreover, to tackle the variations between source and reference at the decoder, we carefully designed a factor graph based low-density parity-check (LDPC) decoder, which automatically detects insertion, deletion and substitution.","PeriodicalId":388717,"journal":{"name":"2013 Data Compression Conference","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115359694","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}
Compression ability is most concerned for multiview video (MVV) transmission system because of its massive amount of data. To improve coding efficiency, the Joint Video Team (JVT) standardization body has developed a joint multiview video coding model (JMVC), in which both intra-view and inter-view prediction techniques are exploited to yield a better coding gain. Therefore, how to prevent from error propagation has become a critical issue in multiview video coding (MVC). Error concealment methods for MVC have been widely studied in recent years, but little research has been conducted on error resilience for MVC. Multiple description coding (MDC) provides a promising solution for robust data transmission over error-prone channels, and has found many applications in monoview video communications. However, these MDC frameworks are not applicable to MVC because its prediction structure involves inter-view prediction. To develop an efficient and robust MVC scheme, a novel MDC algorithm for JMVC based on the mode duplication strategy is proposed in this paper. The input MVV sequence is firstly sub sampled in both horizontal and vertical directions, forming four subsequences, X1p, X1d, X2p, and X2d. Then, X1p and X1d are paired to form description 1, and X2p and X2d are grouped to form description 2, respectively. Secondly, X1p and X2p are directly encoded by separate JMVC encoders. Meanwhile, X1d/X2d adopts the best modes and prediction vectors (PVs) of X1p/X2p in corresponding (same spatial) locations to perform prediction coding. Consequently, neither code for best modes and PVs nor time load for mode decision is needed while coding X1d and X2d. Only coding for the prediction errors is required. Because subsequences in the same description are closely resembled each other, the extra prediction errors introduced by this best mode and PV reuse are negligible. Therefore, the bit rate and computational cost for coding X1d and X2d are greatly reduced. The proposed algorithm has been integrated into JMVC 6.0 and experimented on multiple MVV test sequences. The experimental results have shown that the proposed algorithm outperforms the state-of-the-arts of MDC for MVV and stereoscopic video, achieving improvements of 0.5-3dB in central decode and 0.5-3.5dB in side decode at the same bit rate over a wide range from 500kbps to 6000kbps. Comparing with original JMVC, the proposed algorithm saves about 40% encoding time in average.
{"title":"Mode Duplication Based Multiview Multiple Description Video Coding","authors":"Xiaolan Wang, C. Cai","doi":"10.1109/DCC.2013.106","DOIUrl":"https://doi.org/10.1109/DCC.2013.106","url":null,"abstract":"Compression ability is most concerned for multiview video (MVV) transmission system because of its massive amount of data. To improve coding efficiency, the Joint Video Team (JVT) standardization body has developed a joint multiview video coding model (JMVC), in which both intra-view and inter-view prediction techniques are exploited to yield a better coding gain. Therefore, how to prevent from error propagation has become a critical issue in multiview video coding (MVC). Error concealment methods for MVC have been widely studied in recent years, but little research has been conducted on error resilience for MVC. Multiple description coding (MDC) provides a promising solution for robust data transmission over error-prone channels, and has found many applications in monoview video communications. However, these MDC frameworks are not applicable to MVC because its prediction structure involves inter-view prediction. To develop an efficient and robust MVC scheme, a novel MDC algorithm for JMVC based on the mode duplication strategy is proposed in this paper. The input MVV sequence is firstly sub sampled in both horizontal and vertical directions, forming four subsequences, X1p, X1d, X2p, and X2d. Then, X1p and X1d are paired to form description 1, and X2p and X2d are grouped to form description 2, respectively. Secondly, X1p and X2p are directly encoded by separate JMVC encoders. Meanwhile, X1d/X2d adopts the best modes and prediction vectors (PVs) of X1p/X2p in corresponding (same spatial) locations to perform prediction coding. Consequently, neither code for best modes and PVs nor time load for mode decision is needed while coding X1d and X2d. Only coding for the prediction errors is required. Because subsequences in the same description are closely resembled each other, the extra prediction errors introduced by this best mode and PV reuse are negligible. Therefore, the bit rate and computational cost for coding X1d and X2d are greatly reduced. The proposed algorithm has been integrated into JMVC 6.0 and experimented on multiple MVV test sequences. The experimental results have shown that the proposed algorithm outperforms the state-of-the-arts of MDC for MVV and stereoscopic video, achieving improvements of 0.5-3dB in central decode and 0.5-3.5dB in side decode at the same bit rate over a wide range from 500kbps to 6000kbps. Comparing with original JMVC, the proposed algorithm saves about 40% encoding time in average.","PeriodicalId":388717,"journal":{"name":"2013 Data Compression Conference","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126070036","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}
Transform is one of the most important tools for image/video coding technology. In this paper, novel nonlinear transform generation based on genetic programming is proposed and implemented into H.264/AVC and HEVC reference software to enhance coding performance. The transform procedure itself is coded and transmitted. Despite this overhead, 0.590% (vs. JM18.0) and 1.711% (vs. HM5.0) coding gain was observed in our preliminary experiment.
{"title":"Image Coding Using Nonlinear Evolutionary Transforms","authors":"Seishi Takamura, A. Shimizu","doi":"10.1109/DCC.2013.100","DOIUrl":"https://doi.org/10.1109/DCC.2013.100","url":null,"abstract":"Transform is one of the most important tools for image/video coding technology. In this paper, novel nonlinear transform generation based on genetic programming is proposed and implemented into H.264/AVC and HEVC reference software to enhance coding performance. The transform procedure itself is coded and transmitted. Despite this overhead, 0.590% (vs. JM18.0) and 1.711% (vs. HM5.0) coding gain was observed in our preliminary experiment.","PeriodicalId":388717,"journal":{"name":"2013 Data Compression Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126970808","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}
Embedded quantization is a mechanism through which image coding systems provide quality progressivity. Although the most common embedded quantization approach is to use uniform scalar dead zone quantization (USDQ) together with bit plane coding (BPC), recent work suggested that similar coding performance as that achieved with USDQ+BPC can be obtained with a general embedded quantization (GEQ) scheme than performs fewer quantization stages. Unfortunately, practical approaches of GEQ can not be implemented in bit plane coding engines without substantially modifying their structure. This work overcomes this drawback introducing a 2-step scalar dead zone quantization (2SDQ) scheme compatible with bit plane image coding that provides the same advantages of practical GEQ approaches. Herein, 2SDQ is introduced in the framework of JPEG2000 to demonstrate its viability and efficiency.
{"title":"Low Complexity Embedded Quantization Scheme Compatible with Bitplane Image Coding","authors":"Francesc Auli-Llinas","doi":"10.1109/DCC.2013.35","DOIUrl":"https://doi.org/10.1109/DCC.2013.35","url":null,"abstract":"Embedded quantization is a mechanism through which image coding systems provide quality progressivity. Although the most common embedded quantization approach is to use uniform scalar dead zone quantization (USDQ) together with bit plane coding (BPC), recent work suggested that similar coding performance as that achieved with USDQ+BPC can be obtained with a general embedded quantization (GEQ) scheme than performs fewer quantization stages. Unfortunately, practical approaches of GEQ can not be implemented in bit plane coding engines without substantially modifying their structure. This work overcomes this drawback introducing a 2-step scalar dead zone quantization (2SDQ) scheme compatible with bit plane image coding that provides the same advantages of practical GEQ approaches. Herein, 2SDQ is introduced in the framework of JPEG2000 to demonstrate its viability and efficiency.","PeriodicalId":388717,"journal":{"name":"2013 Data Compression Conference","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114620179","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}
Guillermo de Bernardo, N. Brisaboa, Diego Caro, Michael A. Rodriguez
Summary form only given. In this paper we propose three compact data structures to answer queries on temporal graphs. We define a temporal graph as a graph whose edges appear or disappear along time. Possible queries are related to adjacency along time, for example, to get the neighbors of a node at a given time point or interval. A naive representation consists of a time-ordered sequence of graphs, each of them valid at a particular time instant. The main issue of this representation is the unnecessary use of space if many nodes and their connections remain unchanged during a long period of time. The work in this paper proposes to store only what changes at each time instant. The ttk2-tree is conceptually a dynamic k2-tree in which each leaf and internal node contains a change list of time instants when its bit value has changed. All the change lists are stored consecutively in a dynamic sequence. During query processing, the change lists are used to expand only valid regions in the dynamic k2-tree. It supports updates of the current or past states of the graph. The ltg-index is a set of snapshots and logs of changes between consecutive snapshots. The structure keeps a log for each node, storing the edge and the time where a change has been produced. To retrieve direct neighbors of a node, the previous snapshot is queried, and then the log is traversed adding or removing edges to the result. The differential k2-tree stores snapshots of some time instants in k2-trees. For the other time instants, a k2-tree is also built, but these are differential (they store the edges that differ from the last snapshot). To perform a query it accesses the k2-tree of the given time and the previous full snapshot. The edges that appear in exactly one of these two k2-trees will be the final results. We test our proposals using synthetic and real datasets. Our results show that the ltg-index obtains the smallest space in general. We also measure times for direct and reverse neighbor queries in a time instant or a time interval. For all these queries, the times of our best proposal range from tens of μs to several ms, depending on the size of the dataset and the number of results returned. The ltg-index is the fastest for direct queries (almost as fast as accessing a snapshot), but it is 5-20 times slower in reverse queries. The differential k2-tree is very fast in time instant queries, but slower in time interval queries. The ttk2-tree obtains similar times for direct and reverse queries and different time intervals, being the fastest in some reverse interval queries. It has also the advantage of being dynamic.
{"title":"Compact Data Structures for Temporal Graphs","authors":"Guillermo de Bernardo, N. Brisaboa, Diego Caro, Michael A. Rodriguez","doi":"10.1109/DCC.2013.59","DOIUrl":"https://doi.org/10.1109/DCC.2013.59","url":null,"abstract":"Summary form only given. In this paper we propose three compact data structures to answer queries on temporal graphs. We define a temporal graph as a graph whose edges appear or disappear along time. Possible queries are related to adjacency along time, for example, to get the neighbors of a node at a given time point or interval. A naive representation consists of a time-ordered sequence of graphs, each of them valid at a particular time instant. The main issue of this representation is the unnecessary use of space if many nodes and their connections remain unchanged during a long period of time. The work in this paper proposes to store only what changes at each time instant. The ttk2-tree is conceptually a dynamic k2-tree in which each leaf and internal node contains a change list of time instants when its bit value has changed. All the change lists are stored consecutively in a dynamic sequence. During query processing, the change lists are used to expand only valid regions in the dynamic k2-tree. It supports updates of the current or past states of the graph. The ltg-index is a set of snapshots and logs of changes between consecutive snapshots. The structure keeps a log for each node, storing the edge and the time where a change has been produced. To retrieve direct neighbors of a node, the previous snapshot is queried, and then the log is traversed adding or removing edges to the result. The differential k2-tree stores snapshots of some time instants in k2-trees. For the other time instants, a k2-tree is also built, but these are differential (they store the edges that differ from the last snapshot). To perform a query it accesses the k2-tree of the given time and the previous full snapshot. The edges that appear in exactly one of these two k2-trees will be the final results. We test our proposals using synthetic and real datasets. Our results show that the ltg-index obtains the smallest space in general. We also measure times for direct and reverse neighbor queries in a time instant or a time interval. For all these queries, the times of our best proposal range from tens of μs to several ms, depending on the size of the dataset and the number of results returned. The ltg-index is the fastest for direct queries (almost as fast as accessing a snapshot), but it is 5-20 times slower in reverse queries. The differential k2-tree is very fast in time instant queries, but slower in time interval queries. The ttk2-tree obtains similar times for direct and reverse queries and different time intervals, being the fastest in some reverse interval queries. It has also the advantage of being dynamic.","PeriodicalId":388717,"journal":{"name":"2013 Data Compression Conference","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116154495","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. We address the problem of improving variable-length-to-fixed-length codes (VF codes). A VF code is an encoding scheme that uses a fixed-length code, and thus, one can easily access the compressed data. However, conventional VF codes usually have an inferior compression ratio to that of variable-length codes. Although a method proposed by T. Uemura et al. in 2010 achieves a good compression ratio comparable to that of gzip, it is very time consuming. In this study, we propose a new VF coding method that applies a fixed-length code to the set of rules extracted by the Re-Pair algorithm, proposed by N. J. Larsson and A. Moffat in 1999. The Re-Pair algorithm is a simple off-line grammar-based compression method that has good compression-ratio performance with moderate compression speed. Moreover, we present several experimental results to show that the proposed coding is superior to the existing VF coding.
只提供摘要形式。我们解决了改进变长到定长码(VF码)的问题。VF代码是一种使用固定长度代码的编码方案，因此可以很容易地访问压缩数据。然而，传统的VF码的压缩比通常低于变长码的压缩比。虽然T. Uemura等人在2010年提出的方法实现了与gzip相当的良好压缩比，但它非常耗时。在本研究中，我们提出了一种新的VF编码方法，该方法将固定长度的编码应用于N. J. Larsson和a . Moffat在1999年提出的Re-Pair算法提取的规则集。Re-Pair算法是一种简单的离线基于语法的压缩方法，压缩比性能好，压缩速度适中。此外，我们还给出了几个实验结果，表明所提出的编码优于现有的VF编码。
{"title":"Effective Variable-Length-to-Fixed-Length Coding via a Re-Pair Algorithm","authors":"S. Yoshida, T. Kida","doi":"10.1109/DCC.2013.111","DOIUrl":"https://doi.org/10.1109/DCC.2013.111","url":null,"abstract":"Summary form only given. We address the problem of improving variable-length-to-fixed-length codes (VF codes). A VF code is an encoding scheme that uses a fixed-length code, and thus, one can easily access the compressed data. However, conventional VF codes usually have an inferior compression ratio to that of variable-length codes. Although a method proposed by T. Uemura et al. in 2010 achieves a good compression ratio comparable to that of gzip, it is very time consuming. In this study, we propose a new VF coding method that applies a fixed-length code to the set of rules extracted by the Re-Pair algorithm, proposed by N. J. Larsson and A. Moffat in 1999. The Re-Pair algorithm is a simple off-line grammar-based compression method that has good compression-ratio performance with moderate compression speed. Moreover, we present several experimental results to show that the proposed coding is superior to the existing VF coding.","PeriodicalId":388717,"journal":{"name":"2013 Data Compression Conference","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134000339","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 study compression-aware algorithms, i.e. algorithms that can exploit regularity in their input data by directly operating on compressed data. While popular with string algorithms, we consider this idea for algorithms operating on numeric sequences and graphs that have been compressed using a variety of schemes including LZ77, grammar-based compression, a graph interpretation of Re-Pair, and a method presented by Boldi and Vigna in The Web Graph Framework. In all cases, we discover algorithms outperforming a trivial approach: to decompress the input and run a standard algorithm. We aim to develop an algorithmic toolkit for basic tasks to operate on a variety of compression inputs.
我们研究压缩感知算法，即通过直接操作压缩数据来利用输入数据中的规律性的算法。虽然在字符串算法中很流行，但我们认为这个想法适用于使用各种方案压缩的数字序列和图的算法，包括LZ77、基于语法的压缩、Re-Pair的图解释以及Boldi和Vigna在The Web graph Framework中提出的方法。在所有情况下，我们都发现算法优于一种简单的方法:解压缩输入并运行标准算法。我们的目标是为基本任务开发一个算法工具包，以在各种压缩输入上操作。
{"title":"Algorithms for Compressed Inputs","authors":"Nathan Brunelle, G. Robins, Abhi Shelat","doi":"10.1109/DCC.2013.60","DOIUrl":"https://doi.org/10.1109/DCC.2013.60","url":null,"abstract":"We study compression-aware algorithms, i.e. algorithms that can exploit regularity in their input data by directly operating on compressed data. While popular with string algorithms, we consider this idea for algorithms operating on numeric sequences and graphs that have been compressed using a variety of schemes including LZ77, grammar-based compression, a graph interpretation of Re-Pair, and a method presented by Boldi and Vigna in The Web Graph Framework. In all cases, we discover algorithms outperforming a trivial approach: to decompress the input and run a standard algorithm. We aim to develop an algorithmic toolkit for basic tasks to operate on a variety of compression inputs.","PeriodicalId":388717,"journal":{"name":"2013 Data Compression Conference","volume":"22 6S 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133301207","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 this paper we present efficient parallelization implementations for different stages of the HEVC decoder, which are LCU decoding, deblocking filtering and SAO filtering. Each of the stages are parallelized in separate passes. The LCU decoding is parallelized using Wave front Parallel Processing (WPP). Deblocking and SAO filtering are parallelized by segmenting each picture into separate regions of consecutive LCU rows and processing each of the regions in a concurrent fashion. On a 6 core machine with 6 threads running concurrently, experimental results showed an average accelerating factor of 4.6, 5, 5.35 for the LCU decoding stage and 4.5, 4.9, 5 for deblocking filtering stage and 4, 4.5 and 5 for SAO filtering stages on HD, 1600p and 2160p sequences respectively.
{"title":"Efficient Parallelization of Different HEVC Decoding Stages","authors":"A. Kotra, M. Raulet, O. Déforges","doi":"10.1109/DCC.2013.82","DOIUrl":"https://doi.org/10.1109/DCC.2013.82","url":null,"abstract":"Summary form only given. In this paper we present efficient parallelization implementations for different stages of the HEVC decoder, which are LCU decoding, deblocking filtering and SAO filtering. Each of the stages are parallelized in separate passes. The LCU decoding is parallelized using Wave front Parallel Processing (WPP). Deblocking and SAO filtering are parallelized by segmenting each picture into separate regions of consecutive LCU rows and processing each of the regions in a concurrent fashion. On a 6 core machine with 6 threads running concurrently, experimental results showed an average accelerating factor of 4.6, 5, 5.35 for the LCU decoding stage and 4.5, 4.9, 5 for deblocking filtering stage and 4, 4.5 and 5 for SAO filtering stages on HD, 1600p and 2160p sequences respectively.","PeriodicalId":388717,"journal":{"name":"2013 Data Compression Conference","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132166697","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}
C. Constantinescu, Joseph S. Glider, D. Simha, D. Chambliss
Modern primary storage systems support or intend to add support for real time compression usually based on some flavor of the LZ77 and/or Huffman algorithm. There is a fundamental tradeoff in adding real time (adaptive) compression to such a system: to get good compression the amount of compressed data (the independently compressed block) should be large, to be able to read quickly from random places the blocks should be small. One idea is to let the independently compressed blocks be large but to be able to start decompressing the needed part of the block from a random location inside the compressed block. We explore this idea and compare it with a few alternatives, experimenting with the zlib code base.
{"title":"Random Extraction from Compressed Data - A Practical Study","authors":"C. Constantinescu, Joseph S. Glider, D. Simha, D. Chambliss","doi":"10.1109/DCC.2013.65","DOIUrl":"https://doi.org/10.1109/DCC.2013.65","url":null,"abstract":"Modern primary storage systems support or intend to add support for real time compression usually based on some flavor of the LZ77 and/or Huffman algorithm. There is a fundamental tradeoff in adding real time (adaptive) compression to such a system: to get good compression the amount of compressed data (the independently compressed block) should be large, to be able to read quickly from random places the blocks should be small. One idea is to let the independently compressed blocks be large but to be able to start decompressing the needed part of the block from a random location inside the compressed block. We explore this idea and compare it with a few alternatives, experimenting with the zlib code base.","PeriodicalId":388717,"journal":{"name":"2013 Data Compression Conference","volume":"58 37","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134505642","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}
Jianle Chen, K. Rapaka, Xiang Li, V. Seregin, Liwei Guo, M. Karczewicz, G. V. D. Auwera, J. Solé, Xianglin Wang, Chengjie Tu, Ying Chen, R. Joshi
This paper describes a scalable video codec that was submitted as a response to the joint call for proposals issued by ISO/IEC MPEG and ITU-T VCEG on HEVC scalable extension. The proposed codec uses a multi-loop decoding structure. Several inter-layer texture prediction methods are employed to remove the inter-layer redundancy. Inter-layer prediction is also used when coding enhancement layer syntax elements such as motion parameter and intra prediction mode, to further reduce bit overhead. Additionally, alternative transforms as well as adaptive coefficients scanning are used to code the prediction residues more efficiently. Experimental results are presented to demonstrate the effectiveness of the proposed scheme. When compared to HEVC single-layer coding, the additional rate overhead for the proposed scalable extension is 1.2% to 6.4% to achieve two layers of SNR and spatial scalability.
{"title":"Scalable Video Coding Extension for HEVC","authors":"Jianle Chen, K. Rapaka, Xiang Li, V. Seregin, Liwei Guo, M. Karczewicz, G. V. D. Auwera, J. Solé, Xianglin Wang, Chengjie Tu, Ying Chen, R. Joshi","doi":"10.1109/DCC.2013.27","DOIUrl":"https://doi.org/10.1109/DCC.2013.27","url":null,"abstract":"This paper describes a scalable video codec that was submitted as a response to the joint call for proposals issued by ISO/IEC MPEG and ITU-T VCEG on HEVC scalable extension. The proposed codec uses a multi-loop decoding structure. Several inter-layer texture prediction methods are employed to remove the inter-layer redundancy. Inter-layer prediction is also used when coding enhancement layer syntax elements such as motion parameter and intra prediction mode, to further reduce bit overhead. Additionally, alternative transforms as well as adaptive coefficients scanning are used to code the prediction residues more efficiently. Experimental results are presented to demonstrate the effectiveness of the proposed scheme. When compared to HEVC single-layer coding, the additional rate overhead for the proposed scalable extension is 1.2% to 6.4% to achieve two layers of SNR and spatial scalability.","PeriodicalId":388717,"journal":{"name":"2013 Data Compression Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128896425","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：