CN105915228B - Adaptive mixed data lossless compression method - Google Patents
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Abstract
The present invention relates to a kind of adaptive mixed data lossless compression method, in order to improve the validity of coding, reliability and design.The present invention includes: to be compared by analysis digital signal statistical nature and with the threshold value of setting, realizes the adaptive switching of linear prediction Columbus (Golomb) compiling method and adaptive Golomb compiling method；If needing to predict, then data frame uses linear prediction Golomb compiling method, i.e., carries out linear prediction to data frame, obtain residual signals, residual signals are mapped as positive integer, and to progress Golomb coding after the determining Golomb coding parameter for statistical analysis of the residual signals after mapping.If being not required to predict, data frame uses adaptive Golomb compiling method, i.e., maps data frame, and Golomb coding is carried out after determining Golomb coding parameter for statistical analysis to the data after mapping.The present invention adaptively carries out compressed encoding using different coding mode and coding parameter, to improve the validity and reliability of compression by analyzing the statistics feature of every frame signal.
Description
Technical Field
The present invention relates to signal compression technology, and more particularly, to a self-adaptive hybrid lossless data compression method.
Background
With the development and application of multimedia technology, people obtain information such as texts, images, videos, audios and various animation media, the amount of data to be coded, which needs to be processed and transmitted for transmitting the information, is very large, the storage and transmission of multimedia information are restricted, and the acquisition and transmission of the information are hindered. Therefore, the compression technology and the mass storage technology of data to be encoded are very important for the development of multimedia technology. In recent years, with the advent of mass storage devices and the increase of network transmission bandwidth, physical guarantees are provided for lossless compression, thereby promoting the development thereof. The main contents of lossless compression include framing, prediction, lossless coding, and the like.
Most of the signals encountered in practical applications are not stationary signals, and for the compression processing of random signals, such as intermittent signals, the encoding method using fixed code length will occupy more bits than the encoding method using variable length code, and the compression performance will be reduced.
In view of the above-mentioned drawbacks, the present designer is actively making research and innovation to create a self-adaptive hybrid lossless data compression method, so that the method has industrial application value.
Disclosure of Invention
To solve the above technical problems, it is an object of the present invention to provide an adaptive hybrid lossless data compression method that improves the effectiveness and reliability of compression.
The invention discloses a self-adaptive mixed data lossless compression method, which comprises the following steps:
s1 framing the input data to be coded;
s2, respectively calculating the prediction coefficient of each frame of data to be coded;
s3, comparing the prediction coefficient with a set threshold value, and judging whether each frame of data to be coded needs to be subjected to linear prediction;
if the frame to be coded data needs to be subjected to linear prediction, a linear prediction Golomb coding method is adopted, and the method comprises the following steps:
s3.11, calculating the prediction residual of the frame of data to be coded;
s3.12, performing data mapping on the prediction residual error;
s3.13, carrying out statistical analysis on the mapped data, and determining a selection parameter b of a proper Golomb code;
s3.14, performing Golomb coding on the mapped data by adopting the determined parameter b;
if the frame to-be-coded data does not need linear prediction, adopting a self-adaptive Golomb coding method, comprising the following steps:
s3.21, mapping the data to be coded of the frame to be coded;
s3.22, performing statistical analysis on the mapped data to determine a proper selection parameter b of the Golomb code;
s3.23, Golomb coding is carried out on the mapped data by using the determined parameter b.
Further, S3 compares the prediction coefficient with a set threshold value, and determines whether each frame of data to be encoded needs to be subjected to linear prediction, specifically including:
obtaining autocorrelation coefficients in the Euclidean Yule-Walker equation for linear predictionMinimum prediction error power for 2 nd order prediction
If the minimum prediction error powerThen prediction is carried out;
if the minimum prediction error powerThen no prediction is made;
β is a parameter predetermined according to the signal characteristics.
Further, S3.12 performs data mapping on each frame of the predicted residual of the to-be-encoded data, specifically, the following method is adopted to replace the to-be-encoded value c of each frame of the predicted residual of the to-be-encoded data with the mapped value d:
judging whether the value c to be coded in the prediction residual of the data to be coded of each frame is less than 0 or not,
if the value c to be coded is smaller than 0, the mapping value d is 2 c;
if the value c to be coded is greater than or equal to 0, the mapping value d is 2c + 1;
s3.21, mapping the data to be coded of each frame of data to be coded, and specifically replacing the value e to be coded of each frame of data to be coded with a mapping value f by adopting the following method:
judging whether the value e to be coded in the prediction residual error of the data to be coded of each frame is less than 0 or not,
if the value e to be coded is smaller than 0, the mapping value f is 2 e;
if the value e to be encoded is greater than or equal to 0, the mapping value f is 2e + 1.
Further, the statistical analysis of the mapped data to determine a suitable selection parameter b of the Golomb code in S3.13 specifically includes: different b is selected according to the average value of the prediction residues of each frame,
if the mean of the residual errors mean<m1If b is m1/2；
If m1Mean value of residual error not more than<m2If b is m1；
If m2Mean value of residual error not more than<m3If b is m2；
If the mean residual mean is greater than or equal to m3If b is m3；
S3.22, performing statistical analysis on the mapped data to determine a selection parameter b of a proper Golomb code, which specifically comprises the following steps: different b is selected according to the average value of data to be coded in each frame,
if mean of data to be encoded mean<m1If b is m1/2；
If m1Mean value mean of data to be coded is less than or equal to<m2If b is m1；
If m2Mean value mean of data to be coded is less than or equal to<m3If b is m2；
If the mean of the data to be coded is more than or equal to m3If b is m3。
Preferably, S2 isCalculating the linear prediction coefficient a of each frame to-be-encoded data by using Levenson-Debin Levinson-Durbin algorithmii。
Specifically, S3.11 calculates the prediction residual, and the specific calculation formula of the prediction error is:
where s (n) is the data to be encoded,is a predicted value of data to be encoded;
the predicted value of the data to be coded is predicted by using the past p data s (n) to be coded, and the formula is as follows:
wherein, aiiAre linear prediction coefficients.
Further, the method further includes a decoding process, specifically including:
s1 extracting the flag bit of each frame coding stream;
s2 judges whether or not each frame coded stream is subjected to linear prediction during the coding process,
if linear prediction is performed, then
S2.11, extracting sampling point values, prediction coefficients and selection parameters of the frame coding stream;
s2.12, performing Golomb decoding;
s2.13, restoring the original signal;
if no linear prediction is performed, then
S2.21, extracting the frame coding stream selection parameter;
s2.22, performing Golomb decoding;
s2.23 restores the original signal.
By the scheme, the invention at least has the following advantages:
the invention firstly preprocesses the signal and improves the operation speed. By testing and analyzing the data to be coded of the signal, 2-order linear prediction is adopted. The Golomb coding is used as a main coding mode, the signal is divided into frames, the digital characteristics of each frame of signal are analyzed, different selection parameters are adopted for carrying out Golomb coding, and the self-adaptive performance of compression coding is realized. And calculating the autocorrelation coefficient of each frame, and solving a two-order linear prediction coefficient by a Levinson-Durbin algorithm. Linear prediction is introduced, whether prediction is needed or not is selected on the basis of analyzing a signal frame, compression efficiency is improved, whether linear prediction is needed or not of the frame to be encoded is judged according to a decision threshold, and Golomb encoding of self-adaptive selection parameters is adopted for a prediction frame and a non-prediction frame respectively, so that the flexibility is high, and the compression rate is high.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.
Drawings
FIG. 1 is a flow chart of the encoding of the adaptive hybrid lossless data compression method of the present invention;
fig. 2 is a decoding flow diagram of the adaptive hybrid lossless data compression method of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The invention uses a variable length code, Golomb code, to compress and code the signal. Golomb coding can make the average code length of positive integer data stream to be coded which obeys geometric distribution shortest, shorter codes are used for smaller numbers, longer codes are used for larger numbers, and the optimal variable length codes can be directly given.
The Golomb code of the positive integer n consists of a prefix code and a tail code
The ① prefix code is a unary codeword of q +1 bits, and
② the tail code is the remainder of (n-1)/b
r＝n-1-qb
Binary coding of (2).
Wherein b is 2mParameters are selected for the Golomb code.
Each frame of data to be coded needs to analyze the digital characteristics of the signal frame, and proper selection parameters are selected according to the digital characteristics to realize self-adaptive coding. Through multiple experimental analysis, the Golomb coding method provides Golomb coding of four selection parameters, wherein b is respectively 4, 8,16 and 32, and the maximum bit number of a tail code of the Golomb code is 5. However, for a frame to be encoded with a larger value, if the value b is 32, the value q will be correspondingly larger, the prefix code will occupy a larger number of bits, and the code length will be larger.
Therefore, the invention also introduces a linear prediction technology to selectively predict all the frames to be coded. Namely, the frame to be coded with a smaller numerical value is not predicted and is directly coded; for the 2-order linear prediction of the frame to be coded with a larger numerical value, only the prediction error needs to be coded, and the bit number occupied by the coding is reduced.
Example 1
Referring to fig. 1, a method for adaptive hybrid lossless data compression according to a preferred embodiment of the present invention includes:
s1 frames the input data to be encoded. The framing is a method, a series of input data streams to be coded are divided into a frame, so that the data streams to be coded are conveniently processed, the running speed of a processor is increased, and the framing provides editable capacity, which is an important and necessary characteristic of most digital signal compression algorithms.
S2 calculates the prediction coefficients of the data to be encoded for each frame.
And S3, judging whether each frame of data to be coded needs linear prediction according to the prediction coefficient.
If the frame to be coded data needs to be subjected to linear prediction, then
S3.11 calculates the prediction residual.
And S3.12, mapping the data to be coded of each frame.
S3.13, analyzing the data to be coded of each frame to determine a proper selection parameter b of the Golomb code.
S3.14, Golomb coding is carried out on the data to be coded of each frame.
If the frame to be encoded does not require linear prediction, then
And S3.21, mapping the data to be coded of each frame.
S3.22, analyzing the data to be coded of each frame to determine a proper selection parameter b of the Golomb code.
S3.23, Golomb coding is carried out on the data to be coded of each frame.
In this embodiment, the signal is first preprocessed to increase the operation speed. By testing and analyzing the data to be coded of the signal, 2-order linear prediction is adopted. And calculating the autocorrelation coefficient of each frame, and solving a two-order linear prediction coefficient by a Levinson-Durbin algorithm. And judging whether the frame to be coded needs linear prediction or not according to the judgment threshold. The flow chart of the encoding algorithm is shown in fig. 1.
If the data frame to be coded needs to be predicted, linear prediction is carried out on subsequent signal pairs according to the sampling values of the first two sampling points of the signals, and then residual signals are solved.
If the frame to be coded does not need linear prediction, the frame to be coded is directly mapped, the mapped frame to be coded is analyzed, a proper parameter value b of Golomb coding is selected, and then Golomb coding is carried out. The data frame to be coded also comprises two parts, namely a flag bit and a Golomb code stream. The flag bit has 3 bits, and stores the prediction flag bit and the selection parameter value b.
Example 2
In the adaptive mixed lossless data compression method described in this embodiment, based on embodiment 1, S2 calculates a linear prediction coefficient a of each frame of data to be encoded by using Levinson-Durbin algorithmiiThe method specifically comprises the following steps:
the Euclidean Yule-Walker equation for p-order linear prediction is as follows:
wherein,is an autocorrelation coefficient;
the Euler-Wack Yule-Walker equation has p +1 equations,
when k is 0,1,2, …, pWhen known, get apk[k＝1，２，…，p]Andsampling p +1 unknowns, wherein, apkThe prediction coefficients are used to predict the coefficients,is the minimum error power;
autocorrelation coefficients in the linear predicted euler-waker Yule-Walker equationSolving the minimum prediction error power of 2-order prediction according to Levinson-Durbin algorithm
The recursive formula of the Levenson-Debin Levinson-Durbin algorithm is
Wherein [ k ═ 1,2 …, p ];
two-order prediction, k is 1 and 2, and the prediction coefficient is a11、a22
aki＝ak-1，i+akkak-1，k-i，i＝1，2，…，k-1
Thereby obtaining a prediction coefficient a of two-order linear prediction11、a22。
S3 compares the prediction coefficient with a set threshold value, and determines whether linear prediction is needed for each frame of data to be encoded, which specifically includes:
obtaining autocorrelation coefficients in the Euclidean Yule-Walker equation for linear predictionMinimum prediction error power for 2 nd order prediction
If the minimum prediction error powerThen prediction is carried out;
if the minimum prediction error powerThen no prediction is made;
β is a parameter predetermined according to the signal characteristics.
Specifically, S3.11 calculates the prediction residual, and the specific calculation formula of the prediction error is:
where s (n) is the data to be encoded,is the predicted value of the data to be encoded.
In the linear prediction in this embodiment, all frames to be encoded are selectively predicted. Namely, the frame to be coded with a smaller value is not predicted and is directly coded. For the 2-order linear prediction of the frame to be coded with a larger numerical value, only the prediction error needs to be coded, and the bit number occupied by the coding is reduced.
In each of the above embodiments, S3.12 performs mapping of to-be-encoded data on each frame of to-be-encoded data prediction residual, specifically, the following method is adopted to replace the to-be-encoded value c of each frame of to-be-encoded data prediction residual with the mapping value d:
judging whether the value c to be coded in the prediction residual of the data to be coded of each frame is less than 0 or not,
if the value c to be encoded is smaller than 0, the mapping value d is 2 c.
If the value c to be coded is greater than or equal to 0, the mapping value d is 2c + 1.
S3.21, mapping the data to be coded of each frame of data to be coded, and specifically replacing the value e to be coded of each frame of data to be coded with a mapping value f by adopting the following method:
judging whether the value e to be coded in the prediction residual error of the data to be coded of each frame is less than 0 or not,
if the value e to be encoded is smaller than 0, the mapping value f is 2 e.
If the value e to be encoded is greater than or equal to 0, the mapping value f is 2e + 1.
Considering that Golomb coding is to code a positive integer, it is necessary to map a residual signal to a positive integer and perform Golomb coding on the residual signal. The prediction flag bit of the frame to be coded is 1 bit, the first two sampling points are respectively coded by 16-bit binary, the linear prediction coefficient is respectively coded by 13-bit binary, and the selection parameter b has four conditions, so that two bit positions are occupied. Therefore, the code of the data frame to be coded has two parts, namely a flag bit and a Golomb code stream. The flag bit has 61 bits and is used for storing a prediction flag bit, a prediction coefficient, 2 sampling point values and a selection parameter b of Golomb coding. The Golomb code stream stores all Golomb coding code words of the residual signal.
Each frame of data to be coded needs to analyze the digital characteristics of the signal frame, and proper selection parameters are selected according to the digital characteristics to realize self-adaptive coding. Through multiple experimental analysis, the Golomb coding method provides Golomb coding of four selection parameters, wherein b is respectively 4, 8,16 and 32, and the maximum bit number of a tail code of the Golomb code is 5. However, for a frame to be encoded with a larger value, if the value b is 32, the value q will be correspondingly larger, the prefix code will occupy a larger number of bits, and the code length will be larger.
In each of the above embodiments, the analyzing to-be-encoded data for each frame of prediction residuals and determining a suitable selection parameter b of the Golomb code in S3.13 specifically includes: different b is selected according to the average value of the prediction residues of each frame,
if the mean of the residual errors mean<m1If b is m1/2；
If m1Mean value of residual error not more than<m2If b is m1；
If m2Mean value of residual error not more than<m3If b is m2；
If the mean residual mean is greater than or equal to m3If b is m3；
S3.22, performing statistical analysis on the data to be coded of each frame to determine a selection parameter b of a proper Golomb code, specifically comprising: different b is selected according to the average value of data to be coded in each frame,
if mean of data to be encoded mean<m1If b is m1/2；
If m1Mean value mean of data to be coded is less than or equal to<m2If b is m1；
If m2Mean value mean of data to be coded is less than or equal to<m3If b is m2；
If the mean of the data to be coded is more than or equal to m3If b is m3。
m1、m2、m3And determining the value according to the residual average value or the average value of the data to be coded.
Example 3
The adaptive hybrid lossless data compression method described in this embodiment further includes a decoding step based on embodiment 1, and as shown in fig. 2, the decoding step specifically includes:
s1 extracts the flag bit of each frame encoded stream.
S2 judges whether or not each frame coded stream is subjected to linear prediction during the coding process,
if linear prediction is performed, then
S2.11, extracting sampling point values, prediction coefficients and selection parameters of the frame coding stream.
S2.12 Golomb decoding.
S2.13 restores the original signal.
If no linear prediction is performed, then
S2.21 extracts the frame encoded stream selection parameters.
S2.22 Golomb decoding.
S2.23 restores the original signal.
The adaptive mixed data lossless compression method of the embodiment judges whether the frame is a predicted frame or not according to the flag bit for each input frame code stream,
if the frame is a prediction frame, two prediction coefficients of 2-order prediction and 2 sampling point values are extracted, a selection coefficient b of Golomb coding is used for decoding the coding code stream, and the original signal is recovered.
If the frame is not a prediction frame, extracting a selection coefficient b of Golomb coding, and decoding the code stream to recover the original signal.
In this embodiment, the decoding process is a process of decoding an input encoded code stream to recover an original signal. Because each input frame code stream contains a flag bit and a coding code stream, a prediction flag bit in the flag bit needs to be judged first.
In the above embodiments, if the data to be encoded is analog data, the analog signal needs to be converted into digital data through a/D sampling, the digital data is compressed and encoded, and an encoded code stream is output.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, it should be noted that, for those skilled in the art, many modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (6)
1. An adaptive hybrid lossless data compression method, comprising:
s1 framing the input data to be coded;
s2, respectively calculating the prediction coefficient of each frame of data to be coded;
s3, comparing the prediction coefficient with a set threshold value, and judging whether each frame of data to be coded needs to be subjected to linear prediction;
if the frame to be coded data needs to be subjected to linear prediction, a linear prediction Golomb coding method is adopted, and the method comprises the following steps:
s3.11, calculating the prediction residual of the frame of data to be coded;
s3.12, performing data mapping on the prediction residual error;
s3.13, carrying out statistical analysis on the mapped data, and determining a selection parameter b of a proper Golomb code;
s3.14, performing Golomb coding on the mapped data by adopting the determined parameter b;
if the frame to-be-coded data does not need linear prediction, adopting a self-adaptive Golomb coding method, comprising the following steps:
s3.21, mapping the data to be coded of the frame to be coded;
s3.22, performing statistical analysis on the mapped data to determine a proper selection parameter b of the Golomb code;
s3.23, performing Golomb coding on the mapped data by adopting the determined parameter b;
s3 compares the prediction coefficient with a set threshold value, and determines whether each frame of data to be encoded needs to be subjected to linear prediction, which specifically includes:
obtaining autocorrelation coefficients in the Euclidean Yule-Walker equation for linear predictionMinimum prediction error power for 2 nd order prediction
If it isThen prediction is carried out;
if it isNo prediction is made, wherein β is a parameter predetermined based on signal characteristics.
2. The adaptive hybrid lossless data compression method according to claim 1, wherein S3.12 performs data mapping on the residual to be encoded of each frame, and specifically, the value c to be encoded of the residual to be encoded of each frame is replaced with the mapped value d by the following method:
judging whether the value c to be coded in the prediction residual of the data to be coded of each frame is less than 0 or not,
if the value c to be coded is smaller than 0, the mapping value d is 2 c;
if the value c to be coded is greater than or equal to 0, the mapping value d is 2c + 1;
s3.21, mapping the data to be coded of each frame of data to be coded, and specifically replacing the value e to be coded of each frame of data to be coded with a mapping value f by adopting the following method:
judging whether the value e to be coded in the prediction residual error of the data to be coded of each frame is less than 0 or not,
if the value e to be coded is smaller than 0, the mapping value f is 2 e;
if the value e to be encoded is greater than or equal to 0, the mapping value f is 2e + 1.
3. The adaptive hybrid lossless data compression method as claimed in claim 1, wherein the step S3.13 of performing statistical analysis on the mapped data to determine the selection parameter b of the suitable Golomb code specifically includes: different b is selected according to the average value of the prediction residues of each frame,
if the mean of the residual errors mean<m1If b is m1/2；
If m1Mean value of residual error not more than<m2If b is m1；
If m2Mean value of residual error not more than<m3If b is m2；
If the mean residual mean is greater than or equal to m3If b is m3；
S3.22, performing statistical analysis on the mapped data to determine a selection parameter b of a proper Golomb code, which specifically comprises the following steps: different b is selected according to the average value of data to be coded in each frame,
if mean of data to be encoded mean<m1If b is m1/2；
If m1Mean value mean of data to be coded is less than or equal to<m2If b is m1；
If m2Mean value mean of data to be coded is less than or equal to<m3If b is m2；
If the mean of the data to be coded is more than or equal to m3If b is m3。
4. The adaptive hybrid lossless data compression method as claimed in claim 1, wherein S2 employs Levenson-Debin Levinson-Durbin algorithm to calculate linear prediction coefficient a of each frame to be encodedii。
5. The adaptive hybrid data lossless compression method according to claim 4, wherein S3.11 calculates the prediction residual, and the specific calculation formula of the prediction error is:
where s (n) is the data to be encoded,is a predicted value of data to be encoded;
the predicted value of the data to be coded is predicted by using the past p data s (n) to be coded, and the formula is as follows:
wherein, aiiAre linear prediction coefficients.
6. The adaptive hybrid lossless data compression method as claimed in claim 1, further comprising a decoding process, specifically comprising:
s1 extracting the flag bit of each frame coding stream;
s2 judges whether or not each frame coded stream is subjected to linear prediction during the coding process,
if linear prediction is performed, then
S2.11, extracting sampling point values, prediction coefficients and selection parameters of the frame coding stream;
s2.12, performing Golomb decoding;
s2.13, restoring the original signal;
if no linear prediction is performed, then
S2.21, extracting the frame coding stream selection parameter;
s2.22, performing Golomb decoding;
s2.23 restores the original signal.
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| CN1767395A (en) * | 2004-10-29 | 2006-05-03 | 微软公司 | Lossless adaptive encoding and decoding of integer data |
| CN1783144A (en) * | 2004-10-29 | 2006-06-07 | 微软公司 | Lossless adaptive Golomb/Rice encoding and decoding of integer data using backward-adaptive rules |
| CN102262881A (en) * | 2010-05-24 | 2011-11-30 | 中科开元信息技术(北京)有限公司 | AVS (audio video standard) destructive audio based nondestructive coding and decoding expansion method |
| CN102368385A (en) * | 2011-09-07 | 2012-03-07 | 中科开元信息技术(北京)有限公司 | Backward block adaptive Golomb-Rice coding and decoding method and apparatus thereof |
| CN104217726A (en) * | 2014-09-01 | 2014-12-17 | 东莞中山大学研究院 | Encoding method and decoding method for lossless audio compression |
| CN104349165A (en) * | 2014-09-16 | 2015-02-11 | 上海通途半导体科技有限公司 | High-performance length-variable coding and decoding methods and devices |
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