EP1266455A1 - Procede et appareil de compression sans perte optimises a l'aide d'une pluralite de codeurs - Google Patents

Procede et appareil de compression sans perte optimises a l'aide d'une pluralite de codeurs

Info

Publication number
EP1266455A1
EP1266455A1 EP01912942A EP01912942A EP1266455A1 EP 1266455 A1 EP1266455 A1 EP 1266455A1 EP 01912942 A EP01912942 A EP 01912942A EP 01912942 A EP01912942 A EP 01912942A EP 1266455 A1 EP1266455 A1 EP 1266455A1
Authority
EP
European Patent Office
Prior art keywords
lossless
data
coders
data stream
compression
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP01912942A
Other languages
German (de)
English (en)
Other versions
EP1266455A4 (fr
Inventor
Igor V. Ternovskiy
Aleksandr A. Devivye
Joseph Rotenberg
Freddie Lin
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Physical Optics Corp
Original Assignee
Physical Optics Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Physical Optics Corp filed Critical Physical Optics Corp
Publication of EP1266455A1 publication Critical patent/EP1266455A1/fr
Publication of EP1266455A4 publication Critical patent/EP1266455A4/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
    • H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction

Definitions

  • the present invention is directed to data compression techniques and, more particularly, to a method and apparatus for selecting among different types of lossless compression coders to optimize system performance.
  • Data compression operates to minimize the number of bits used to store or transmit information and encompasses a wide array of software and hardware compression techniques. Notably, depending on the type of data to be compressed and any number of other factors, particular compression techniques can provide markedly superior performance in terms of compression ratio and coding speed.
  • data compression includes taking a stream of symbols or phrases and converting them into codes that are smaller (in bit length) than the original data.
  • Known compression techniques and algorithms can be divided into two major families including lossy and lossless.
  • Lossy data compression can be used to greatly increase data compression ratios; however, increased compression comes at the expense of a certain loss in accuracy. As a result, lossy compression typically is implemented only in those instances in which some data loss is acceptable. For example, lossy compression is used effectively used when applied to digitized voice signals and graphics images.
  • Lossless compression is a family of data compression that utilizes techniques designed to generate an exact duplicate of the input data stream after a compression/decompression cycle. This type of compression is necessary when storing database records, word processing files, etc. , where loss of information is absolutely unacceptable.
  • the present invention is directed to lossless data compression.
  • Some lossless compression algorithms use information theory to generate variable length codes when given a probability table for a given set of symbols.
  • the decision to output a certain code for a particular symbol or set of symbols is based on a model.
  • the model is a set of rules used to process input messages, and in response, determine which codes to output.
  • An algorithm or program uses the model to analyze the symbols (e.g., determine a probability associated with the symbol) and then outputs an appropriate code based on that processing.
  • a model should be selected that predicts symbols or phrases with high probabilities because symbols or messages that have a high probability have a low information content, and therefore require fewer bits to encode.
  • the next step is to encode the symbols using a particular lossless coder.
  • lossless compression coders can be grouped according to whether they implement statistical modeling or dictionary-based modeling. Statistical modeling reads and encodes a single symbol at a time using the probability of the character's appearance, while dictionary-based modeling uses a single code to replace strings of symbols.
  • dictionary-based modeling the model is significantly more important than in statistical-based modeling because problems associated with encoding every symbol are significantly reduced.
  • S-F coding One form of statistical data compression is known as Shannon-Fano (S-F) coding.
  • S-F coding was developed to provide variable-length bit coding so as to allow coding symbols with exactly (or a close approximation to) the number of bits of information that the message or symbol contains.
  • S-F coding relies on knowing the probability of each symbol's appearance in a message. After the probabilities are determined, a table of codes is constructed with each code having a different number of bits (advantageously, symbols with low probabilities have more bits).
  • One problem with a coding technique such as this is that it creates variable length codes that have an integral number of bits, even though the information to be coded likely will require a non- integral number of bits.
  • Huffman coding is similar to S-F coding in that it creates variable length codes that are an integral number of bits, but it utilizes a completely different algorithm.
  • S-F and Huffman codings are close in performance but Huffman coding, it has been determined, always at least equals the efficiency of S-F coding so it is therefore preferred, especially since both algorithms take a similar amount of processing power.
  • Huffman coding is relatively easy to implement and economical for both coding and decoding, it is inefficient due to its use of an integral number of bits per code as in S-F coding. If a particular symbol is determined to have an information content (i.e.
  • a Huffman coder will generate a code having a bit count that is either one or two bits.
  • the optimal code size would be 0.15 bits; however, Huffman or S-F coding likely would assign a one bit code to the symbol, which is six times larger than necessary.
  • Arithmetic coding replaces a stream of input symbols with a single floating point output number, and bypasses the step of replacing an input symbol with a specific code.
  • an arithmetic code is not limited to being optimal only when the symbol probabilities are integral powers of one-half (which is most often not the case), it attains the theoretical entropy of the symbol to be coded, thus maximizing compression efficiency for any known source.
  • the entropy of a given character is 1.5 bits
  • arithmetic coding uses 1.5 bits to encode the symbol, an impossibility for Huffman and Shannon-Fano coding.
  • arithmetic coding is extremely efficient, it consumes rather large amounts of computing resources, both in terms of CPU power and memory. This is due to the fact that sophisticated models that demand a significant amount of memory must be built, and that the algorithm itself requires a significant amount of computational operations.
  • dictionary-based compression algorithms replace occurrences of particular phrases (i.e., groups of bytes) in a data stream with a reference to a previous occurrence of those phrases.
  • dictionary -based algorithms do not encode single symbols. Rather, dictionary -based compression techniques encode variable length strings of symbols as single "tokens. " It is these tokens that form an index to a phrase dictionary. Because the tokens are smaller than the phrases they replace, compression occurs.
  • dictionary-based compression schemes Two main classes of dictionary -based compression schemes are known as the LZ77 and LZ78 compression algorithms of the Lempel-Ziv family of compression coders.
  • dictionary-based coding is utilized extensively in desktop general purpose compression and has been implemented by CompuServe Information Service to encode bit-mapped graphical images.
  • the GIF format uses a LZW variant to compress repeated sequences and screen images.
  • dictionary-based compression techniques are very popular forms of compression, the disadvantage of such algorithms is that a more sophisticated data structure is needed to handle the dictionary.
  • the present invention is directed to a method and apparatus that determines which of a number of embedded coding schemes will optimally compress different portions of an incoming data stream.
  • the method of the preferred embodiment is designed to accommodate a data stream characterized by having different packets of information (e.g., from sources unknown to the encoders) each of which may have different associated statistics.
  • a method of lossless compression of a stream of data includes providing a plurality of lossless coders. The method then includes selecting one of the lossless coders to compress the stream of data, and thereafter encoding the data stream with the selected lossless coder.
  • a method of lossless compression of a stream of data includes using a plurality of lossless coders to compress a test portion of the data stream. Once the test portion is compressed, the method determines a performance characteristic associated with each of the lossless coders. Then the method includes selecting one of the lossless coders based on the determining step and encoding a first portion of the data stream with the selected coder. Thereafter, the method includes repeating the using, determining, selecting and encoding steps for another test portion and a second portion of the data stream. Notably, the repeating step may include selecting a different one of the lossless coders.
  • each of the lossless coders uses (1) a compression technique, and (2) a number of bits per word determined by the selecting step, in the encoding step.
  • the compression technique is one of Arithmetic coding, Huffman coding and LZ coding.
  • an apparatus for lossless data compression includes an interface to receive a stream of data.
  • the apparatus includes a plurality of lossless coders and a processor.
  • each lossless coder separately compresses a test portion of the data stream and, in response, the processor determines a performance characteristic associated with each of the lossless coders, and then selects, based on the performance characteristics, one of the lossless coders to encode at least a first portion of the data stream.
  • the performance characteristic includes at least one of compression ratio and duration of the compression of the test portion for a corresponding lossless coder.
  • the encoder includes a plurality of processors and each of the lossless coders corresponds to one of the processors, and wherein the lossless coders compress the same test portion in parallel.
  • FIG. 1 is a flow diagram showing the general operation of a method of the preferred embodiment
  • FIG. 1A is a chart showing an array of lossless coders used in the method shown in FIG. 1;
  • FIG. 2 is a generic block diagram showing an encoding/decoding system of the preferred embodiment.
  • FIG. 3 is a schematic diagram showing the data stream as it is encoded/decoded by the system shown in FIG. 2.
  • DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Referring to Fig. 1, a method 10 includes, after initialization and start-up at Step 12, inputting data to the system at Step 14.
  • the data input at Step 14 may be synchronous or asynchronous data.
  • the data stream may be received from unspecified sources such as sensors that monitor temperature, pressure, etc. of a subject (e.g. , telemetric data gathered in military applications) and that continuously transmit readings to the system encoder (described below) of the preferred embodiment.
  • Unspecified data necessarily implies that the statistics associated with the data are random, and therefore unlike known systems that perform compression with a single type of encoder based on knowledge regarding the statistics of the data, the preferred embodiment is able to efficiently code a data stream comprised of different types of data. Other types of applications where this type of random data may originate from multiple sources include hospital monitoring applications, chemical factories, nuclear plants, and others.
  • the data is continuously input to the system, it is transmitted to a division on communication block where method 10, at Step 16, processes the data by dividing or framing the data for further communication thereof.
  • the division on communication block is implemented by method 10 in conventional fashion.
  • the data is pre-processed which may include generating a histogram indicating the statistics associated with the data framed in Step 16.
  • method 10 adds synchronization and header codes at Step 20, as required to further process and identify the data bits in the data stream.
  • the data is transmitted to a plurality of coders that provide lossless compression.
  • method 10 codes a test portion of the data stream with a plurality of lossless coders and determines system performance criteria associated with each of the coders.
  • the coders used to code the portion of the data in Step 22 are shown at 32 in chart 30 of Fig. 1A.
  • the columns of the charts indicate different types of lossless coding techniques/algorithms which may include Huffman coding, Arithmetic coding, Lempel-Ziv coding, as well as variants of these and other known coding techniques.
  • the method also compares the output of the coding techniques with the data stream without encoding/compressing because, in certain circumstances, uncompressed data may be optimum.
  • the columns comprise lossless coding techniques.
  • the rows comprise different designations for the number of bits per word, bpw 1-m, may be used to encode the data.
  • the bits per word associated with the interface may be set to be eight bits, ten bits, etc., for example.
  • method 10 codes a portion of the data with n x m number of lossless coders.
  • Step 22 is performed for a test period of time or amount of data to determine which of the lossless coders achieves optimum system performance prior. Thereafter, the data is encoded (described below).
  • the test compression performed by coders 32 in Step 22 preferably is conducted in parallel to quickly compile data corresponding to each of the lossless coders.
  • Parallel coding of the test data is possible due to the fact that computing power has become so inexpensive that the benefits (e.g., in terms of encoding speed) greatly outweigh the costs.
  • each of coders 32 shown in Fig. 1A may code the test data sequentially over a designated period of time to produce the corresponding performance data. Although not preferred, such a sequential test may be performed when computing power is at a premium.
  • the performance criteria generated in Step 22 for nine different lossless coders (three different word lengths x three different coding techniques) is shown.
  • the input bit rate is set at a predetermined value
  • the output bit rate although preferably set based on the transmission medium employed, may be continuously updated based on feedback information regarding the lossless coder employed. Optimally, the output bit rate will be made as small as possible.
  • Table 1 after compressing a test amount of data, an output file size in bytes, a compression ratio, and a time to encode are each determined for a designated speed input (kbit/second) and speed output (kbit/second).
  • Huffman coding achieves a compression ratio of 1.8272
  • Lempel-Ziv coding achieves a ratio of 2.505
  • arithmetic coding achieves a ratio of 2.7724.
  • the time to encode the test data for each of these algorithms is 128 seconds, 522 seconds, and 1,582 seconds, respectively.
  • the selection made in Step 24 typically is not based solely on compression ratio realized but rather the selection is made based on a combination of overall processing time and compression ratio performance characteristics.
  • Table 1 arithmetic coding, for eight bits per word, achieves a compression ratio of 2.7724 which is greater than the compression ratio achieved for Lempel-ZIV coding, 2.505.
  • arithmetic coding takes more than fifteen minutes longer to encode than the Lempel-Ziv lossless coder.
  • method 10 likely would select the Lempel-Ziv coder in Step 24.
  • method 10 may decide to send the data uncompressed. This decision depends on, among other things, user requirements.
  • the input clock rate indicated in Table 1 is dependent upon both the media over which the data is transmitted (internet, for example) and the type of coding algorithm implemented.
  • tprocessing includes the time duration associated with compressing the data, system delay, etc.
  • tc is the time to transmit the data and equals the size of the file divided by the compression ratio and by the output speed, i.e. , the bit rate, and reflects the time savings achieved by compressing the data.
  • the compression ratio (CR) being equal to the input file size divided by the output file size.
  • method 10 encodes the data with the selected coder for, preferably, a predetermined amount of time. Thereafter, the program returns to Step 22 to code a new test portion of the data stream and select an optimum coder for encoding the next portion of the data stream. This operation may require implementing a different lossless coder shown in Chart 30.
  • a system 40 for performing method 10 includes an encoder 41 having an input interface 42 which includes a clock input Cl and a data input Dl for receiving a data stream 43 that is either synchronous or asynchronous.
  • Interface 42 is coupled to a digital signal processor (DSP) chip 46 via input and output data-control-synchronous input/output lines 44.
  • DSP 46 preferably performs steps 16 and 18 in method 10 shown in FIG. 1 to frame the data and prepare it for compression.
  • the output of DSP 46 is coupled to computer 50 via a PCI bus 48 that communicates the framed data to the computer.
  • Computer 50 preferably, adds appropriate header codes to the data stream to indicate different packets of data and operates to encode/compress the test data with each lossless coder shown in chart 30.
  • computer 50 may comprise a plurality of processors each capable of encoding/compressing data for a corresponding one of the lossless coders implemented from the grid 30 in Fig. 1A.
  • a single computer 50 could be used to implement the test compression for each of the lossless coders 32 in a sequential fashion for predetermined period of time.
  • Computer 50 may also be used to add header codes to the data to ensure that the file will be decompressed correctly.
  • the compressed data is then transmitted via PCI bus 48 to the DSP chip 46 to divide the data as necessary for the specific communication system implemented. This process may involve buffering the data by inserting empty blocks and/or deleting existing blocks. Thereafter, particular synchronization codes may be added and the data stream is transmitted along the input/output lines back to interface 42.
  • the particular interface code settings include designating the number of bits/word, the number of words per frame, synchronizing codes, a control sum, etc.
  • Interface 42 then outputs the data stream on line D2, so that it may be transmitted over a medium 52 such as the internet.
  • the output clock rate C2 is set by the operator and is dependent upon the type of medium 52 implemented.
  • the decoder 53 of system 40 includes an interface 54 having a data input D3 for receiving the compressed data from medium 52 at a clock rate C3 that corresponds to clock rate C2 output from interface 42. Notably, clocks C2 and C3 are optional.
  • Interface 54 transmits the compressed data stream via data-control- synchronous lines 56, while the POC synchronization added by encoder 41 is deleted.
  • a DSP chip 58 detects the header code(s) and removes empty blocks from the data stream.
  • the data processed by DSP chip 58 is then transmitted to a computer 62 via a PCI bus 60.
  • Computer 62 decompresses the data, and, preferably, implements conventional control sum check (CSC) comparison techniques.
  • CSC control sum check
  • Additional error detection or error correction coders may also be implemented by computer 62.
  • a Reed-Solomon error correction coder is standard for communication networks and is preferably included. Notably, the above- described processing operations may be performed either by computer(s) 50, 62 or DSP chips 46, 48, but the preferred implementation has been described.
  • FIG. 3 A representation of the method steps described in FIG. 1 and performed by the apparatus shown in Fig. 2 is shown schematically in FIG. 3 for a telemetric data stream.
  • the arrow labeled A on the right-side of FIG. 3 indicates the encoding process, while the arrow B along the left-side of the data shown in FIG. 3 indicates the decoding process.
  • data stream 43 is input to interface 42 (FIG. 2) and then framed in a preassigned fashion into packet portions 64, 66 (preferably several kilobytes, e.g., two 8k portions), preferably by DSP 46. Thereafter, portions 64, 66 are compressed into, for example, a 4.5k block 68 and a 4.3k block 70.
  • a header 73, 75 is added to the blocks of the packets (with the interface information described above) to create blocks 72, 74, respectively. Then, the blocks are buffered to construct a buffered and compressed packet 76 which may be divided again if necessary to create stream 78. Then, POC synchronization is added by DSP chip 46 and new data stream 80 may be transmitted to decoder 53 via, for example, the internet 52 (FIG. 2) where it is decoded as described above.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)
  • Compression Of Band Width Or Redundancy In Fax (AREA)

Abstract

Un procédé de compression sans perte d'un train de données consiste tout d'abord à utiliser une pluralité de codeurs sans pertes afin de comprimer une partie d'essai du train de données (30). Une fois la partie d'essai comprimée, le procédé détermine une ou des caractéristiques de fonctionnement associées à chacun des codeurs sans perte (32). Ensuite, le procédé sélectionne un des codeurs sans perte sur la base de la ou des caractéristiques de fonctionnement et il code une première partie du train de données avec le codeur sélectionné. Ensuite, le procédé consiste à répéter les étapes d'utilisation, de détermination, de sélection et de codage pour une autre partie d'essai et une seconde partie du train de données. Il est à noter que l'étape de répétition peut comprendre la sélection d'un autre codeur sans perte.
EP01912942A 2000-02-25 2001-02-22 Procede et appareil de compression sans perte optimises a l'aide d'une pluralite de codeurs Withdrawn EP1266455A4 (fr)

Applications Claiming Priority (3)

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US513309 1990-04-20
US51330900A 2000-02-25 2000-02-25
PCT/US2001/005722 WO2001063772A1 (fr) 2000-02-25 2001-02-22 Procede et appareil de compression sans perte optimises a l'aide d'une pluralite de codeurs

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EP1266455A1 true EP1266455A1 (fr) 2002-12-18
EP1266455A4 EP1266455A4 (fr) 2003-06-18

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EP (1) EP1266455A4 (fr)
JP (1) JP2003524983A (fr)
CN (1) CN1426629A (fr)
AU (1) AU2001241672A1 (fr)
TW (1) TWI273779B (fr)
WO (1) WO2001063772A1 (fr)

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US20010047473A1 (en) 2000-02-03 2001-11-29 Realtime Data, Llc Systems and methods for computer initialization
US9143546B2 (en) 2000-10-03 2015-09-22 Realtime Data Llc System and method for data feed acceleration and encryption
US8692695B2 (en) 2000-10-03 2014-04-08 Realtime Data, Llc Methods for encoding and decoding data
US7386046B2 (en) 2001-02-13 2008-06-10 Realtime Data Llc Bandwidth sensitive data compression and decompression
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CN102595496A (zh) * 2012-03-08 2012-07-18 西北大学 用于无线传感节点感知数据的上下文自适应商余编码方法
US9681332B2 (en) * 2014-06-26 2017-06-13 Intel Corporation Compression configuration identification
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JP6726690B2 (ja) * 2015-06-15 2020-07-22 アスカバ・インコーポレイテッドＡｓｃａｖａ， Ｉｎｃ． 基本データシーブを用いて無損失削減されたデータに対する多次元検索、コンテンツ連想的な取出し、ならびにキーワードベースの検索および取出しの実行
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CN111314277A (zh) * 2019-11-13 2020-06-19 谢卓鹏 一种基于gnss大数据的压缩方法
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WO2001063772A1 (fr) 2001-08-30
JP2003524983A (ja) 2003-08-19
TWI273779B (en) 2007-02-11
AU2001241672A1 (en) 2001-09-03
EP1266455A4 (fr) 2003-06-18
CN1426629A (zh) 2003-06-25

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