This paper presents a class of new lossless data compression algorithms. Each algorithm in this class first transforms the original data to be compressed into an irreducible table representation and then uses an arith...
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This paper presents a class of new lossless data compression algorithms. Each algorithm in this class first transforms the original data to be compressed into an irreducible table representation and then uses an arithmetic code to compress the irreducible table representation. From the irreducible table representation, one can fully reconstruct the original data by performing multistage parallel substitution. A set of rules is described on how to perform hierarchical transformations from the original data to irreducible table representations. Theoretically, it is proved that all these algorithms outperform any finite state sequential compression algorithm and hence achieve the ultimate compression rate for any stationary and ergodic source. Furthermore, experiments on several standard images show that even a simple algorithm in this class, the so-called multi-level pattern matching algorithm, outperforms the Lempel-Ziv algorithms and arithmetic codes.
Data compression and prediction are closely related. Thus prediction methods based on data compression algorithms have been suggested for the branch prediction problem. In this work we consider two universal compressi...
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Data compression and prediction are closely related. Thus prediction methods based on data compression algorithms have been suggested for the branch prediction problem. In this work we consider two universal compression algorithms: prediction by partial matching (PPM), and a recently developed method, context tree weighting (CTW). We describe the prediction algorithms induced by these methods. We also suggest adaptive algorithms variations of the basic methods that attempt to fit limited memory constraints and to match the non-stationary nature of the branch sequence. Furthermore, we show how to incorporate address information and to combine other relevant data. Finally, we present simulation results for selected programs from the SPECint95, SYSmark/32, SYSmark/NT, and transactional processing benchmarks. Our results are most promising in programs with difficult to predict branch behavior.
This paper is devoted to the study of the problem of running compression algorithms in the encrypted domain, using a (somewhat) fully homomorphic encryption (FHE) scheme. We do so with a particular focus on conservati...
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ISBN:
(数字)9781538624876
ISBN:
(纸本)9781538624883
This paper is devoted to the study of the problem of running compression algorithms in the encrypted domain, using a (somewhat) fully homomorphic encryption (FHE) scheme. We do so with a particular focus on conservative compression algorithms. Despite of the encrypted domain Turingcompleteness which comes with the magic of FHE operators, we show that a number of subtleties crop up when it comes to running compression algorithms and, in particular, that guaranteed conservative compression is not possible to achieve in the FHE setting. To illustrate these points, we analyze the most elementary conservative compression algorithm of all, namely Run-Length Encoding (RLE). We first study the way to regularize this algorithm in order to make it (meaningfully) fit within the constraints of a FHE execution. Secondly, we analyze it from the angle of optimizing the resulting structure towards (as much as possible) FHE execution efficiency. The paper is concluded by concrete experimental results obtained using the Fan-Vercauteren cryptosystem as well as the Armadillo FHE compiler. It is also this paper intent to share the concrete return on experience we gained in attempting to run a simple yet practically significant algorithm over FHE.
To solve some problems of data storage and transmission related to the systems for automatic interpretation of the electrocardiogram (ECG) various classes of reversible and irreversible compression algorithms have bee...
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To solve some problems of data storage and transmission related to the systems for automatic interpretation of the electrocardiogram (ECG) various classes of reversible and irreversible compression algorithms have been tested on the database produced by CSE. Errors of the reconstructed signal have been evaluated by means of PRD, RMS and other synthetic parameters together with the visual analysis by a cardiologist. Tracing rebuilt from the compressed signal has been analyzed using the MEANS program of automatic interpretation, and diagnostic answers with the related measurements have been compared with the results obtained on the original signal. Inconsistent issues have been reexamined by a cardiologist to determine the artifacts and the type and amount of information lost because of compression. The results define the limits of the different classes of compression methods in relation to the accuracy expected in the diagnosis.< >
Today, electronically controlled modules or electronic control units (ECUs) are part of any automobile. These are deployed for managing critical and not so critical operations in automotives. Thus, a lot of communicat...
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ISBN:
(纸本)9781467322706;9781467322713
Today, electronically controlled modules or electronic control units (ECUs) are part of any automobile. These are deployed for managing critical and not so critical operations in automotives. Thus, a lot of communication occurs between these ECUs. Use of Data Reduction (DR) algorithm reduces the amount of data transferred in a given time thus improving the performance of the communication between the ECUs. Supriya Kelkar and Raj Kamal have proposed and implemented Quotient Remainder compression (QRC) algorithm for Controller Area Network [1] and showed the improvement in the range for the parameters compared to the earlier Enhanced Data Reduction (EDR) algorithm [2]. In this paper, authors of [1] have developed two more versions of QRC algorithm. Here, all the three versions of QRC algorithm are compared and their merits and demerits are stated.
This paper reviewed the recent progress in the field of electrocardiogram (ECG) compression and compared the efficiency of some compression algorithms. By experimenting on the 500 cases of ECG signals from the ECG dat...
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This paper reviewed the recent progress in the field of electrocardiogram (ECG) compression and compared the efficiency of some compression algorithms. By experimenting on the 500 cases of ECG signals from the ECG database of China, it obtained the numeral indexes for each algorithm. Then by using the automatic diagnostic program developed by Shanghai Zhongshan Hospital, it also got the parameters of the reconstructed signals from linear approximation distance threshold (LADT), wavelet transform (WT), differential pulse code modulation (DPCM) and discrete cosine transform (DCT) algorithm. The results show that when the index of percent of root mean square difference(PRD) is less than 2.5%, the diagnostic agreement ratio is more than 90%; the index of PRD cannot completely show the damage of significant clinical information; the performance of wavelet algorithm exceeds other methods in the same compression ratio (CR). For the statistical result of the parameters of various methods and the clinical diagnostic results, it is of certain value and originality in the field of ECG compression research.
This textbook is aimed at computer science students. The authors perform compression experiments throughout the text which are of two types: data sequencing and archival data filing. The book has good discussions of l...
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This textbook is aimed at computer science students. The authors perform compression experiments throughout the text which are of two types: data sequencing and archival data filing. The book has good discussions of lossless compression algorithms from an algorithmic point of view and is recommended
Time-varying meshes (TVMs), that is mesh sequences with varying connectivity, are a greatly versatile representation of shapes evolving in time, as they allow a surface topology to change or details to appear or disap...
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Time-varying meshes (TVMs), that is mesh sequences with varying connectivity, are a greatly versatile representation of shapes evolving in time, as they allow a surface topology to change or details to appear or disappear at any time during the sequence. This, however, comes at the cost of large storage size. Since 2003, there have been attempts to compress such data efficiently. While the problem may seem trivial at first sight, considering the strong temporal coherence of shapes represented by the individual frames, it turns out that the varying connectivity and the absence of implicit correspondence information that stems from it makes it rather difficult to exploit the redundancies present in the data. Therefore, efficient and general TVM compression is still considered an open problem. We describe and categorize existing approaches while pointing out the current challenges in the field and hint at some related techniques that might be helpful in addressing them. We also provide an overview of the reported performance of the discussed methods and a list of datasets that are publicly available for experiments. Finally, we also discuss potential future trends in the field.
The Lempel-Ziv (LZ) 4 compression algorithm, widely used in data transmission and storage, faces the challenge of high-speed implementation and increased complexity in the era of big data. Therefore, this paper propos...
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The Lempel-Ziv (LZ) 4 compression algorithm, widely used in data transmission and storage, faces the challenge of high-speed implementation and increased complexity in the era of big data. Therefore, this paper proposes a single-core parallel architecture for LZ4 algorithm with high throughput and low complexity. Firstly, to enhance throughput, two innovative approaches are introduced from the perspective of parallelism and frequency with an acceptable compression ratio loss: each parallelization window is restricted to performing a single match, bridging the gap between actual and theoretical parallelism;the feedback loop in the circuit is broken by utilizing the spatial correlation between adjacent matches for higher frequency. Secondly, two optimization schemes are employed on resource-consuming modules to achieve low complexity. Multi-port hash tables using Live Value Table (LVT) are improved based on inherent data characteristics, significantly reducing the hardware resource consumption while ensuring excellent scalability on hash table depth and frequency. The match comparison operation is moved ahead, further reducing the logic resources by 64.36%. Finally, our design is implemented on FPGA and ASIC platforms. Experimental results on FPGA demonstrate that the proposed architecture achieves a throughput of 17.39 Gb/s, exhibiting a 2.86x improvement over the state-of-the-art, along with a 6.46x enhancement in area efficiency. Further optimizations including Canonic Signed Digit (CSD) coding and computational reuse on the ASIC platform result in a 45x improvement in area efficiency.
Data compression has been widely used by datacenters to decrease the consumption of not only the memory and storage capacity but also the interconnect bandwidth. Nonetheless, the CPU cycles consumed for data compressi...
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Data compression has been widely used by datacenters to decrease the consumption of not only the memory and storage capacity but also the interconnect bandwidth. Nonetheless, the CPU cycles consumed for data compression notably contribute to the overall datacenter taxes. To provide a cost-efficient data compression capability for datacenters, Intel has introduced QuickAssist Technology (QAT), a PCIe-attached data-compression accelerator. In this work, we first comprehensively evaluate the compression/decompression performance of the latest on-chip QAT accelerator and then compare it with that of the previous-generation off-chip QAT accelerator. Subsequently, as a compelling application for QAT, we take a Linux memory optimization kernel feature: compressed cache for swap pages (zswap), re-implement it to use QAT efficiently, and then compare the performance of QAT-based zswap with that of CPU-based zswap. Our evaluation shows that the deployment of CPU-based zswap increases the tail latency of a co-running latency-sensitive application, Redis by 3.2-12.1x, while that of QAT-based zswap does not notably increase the tail latency compared to no deployment of zswap.
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