CN105915227A - Adaptive mixed data lossless compression system - Google Patents
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Abstract
The invention relates to an adaptive mixed data lossless compression system, which improves effectiveness and reliability of coding. The adaptive mixed data lossless compression system comprises a statistic analysis module which analyzes statistic characteristics of a signal to be coded and performs comparison with a set threshold value; adaptive switching between a linear prediction Golomb coding method and an adaptive Golomb coding method is realized; if the linear prediction Golomb coding method is applicable, a linear prediction module performs linear prediction on a signal to be coded to obtain a residual signal which is mapped as a positive integer; a statistic determination module determines the Golomb coding parameter according to the mapped residual signal and then performs Golomb coding; if the adaptive Golomb coding method is applicable, the statistic judgment module performs direct mapping on the signal to be coded, determines the Golomb coding parameter according to the mapped data and then performs Golomb coding. The adaptive mixed data lossless compression method analyzes digit statistics characteristics of a signal of each frame and adaptively adopts various coding methods and coding parameters to perform compression coding so as to improve effectiveness and reliability of the compression.
Description
Technical Field
The present invention relates to signal compression technology, and more particularly, to an adaptive hybrid lossless data compression system.
Background
With the development and application of multimedia technology, people obtain information such as texts, images, videos, audios and various animation media, and the amount of data to be processed and transmitted for transmitting the information is very large, so that the storage and transmission of multimedia information are restricted, and the acquisition and transmission of the information are hindered. Data compression technology and mass storage technology are therefore of great importance to 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 system, so that the system has higher industrial utilization 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 system that improves the effectiveness and reliability of compression.
The invention relates to a self-adaptive mixed data lossless compression system, which comprises:
the central controller comprises a statistical analysis module, a linear prediction module, a statistical decision module and a Golomb coding module;
the statistical analysis module compares the signal analysis statistical characteristics of the signal to be coded with a set threshold value to judge whether the signal to be coded needs to be predicted or not,
if prediction is needed, the statistical analysis module outputs a signal to be coded to the linear prediction module; the linear prediction module performs linear prediction on a signal to be coded to obtain a residual signal, and outputs the residual signal to the statistical decision module; the statistical decision module obtains a Golomb coding parameter based on the residual signal, outputs the Golomb coding parameter to the Golomb coding module, and the Golomb coding module codes the residual signal;
if the prediction is not needed, the statistical analysis module outputs a signal to be coded to the statistical judgment module; the statistical decision module obtains a Golomb coding parameter based on a signal to be coded, outputs the Golomb coding parameter to the Golomb coding module, and codes the signal to be coded by the Golomb coding module.
Furthermore, the central controller also comprises a prediction decision module, an inverse linear prediction module and a Golomb decoding module;
a prediction judgment module for judging whether the code stream is subjected to linear prediction,
if no linear prediction exists, the coding code stream is output to a Golomb decoding module, and a signal to be coded is recovered through processing of the Golomb decoding module;
if linear prediction is carried out, the coding code stream is output to an inverse linear prediction module for processing, then output to a Golomb decoding module, and a signal to be coded is recovered through processing of the Golomb decoding module.
Further, the statistical analysis module comprises a prediction coefficient calculation unit and a comparison unit,
the prediction coefficient calculation unit is used for running a Levenson-Debin algorithm to solve a prediction coefficient of two-order linear prediction of a signal to be coded and an output autocorrelation coefficient;
the comparison unit is used for obtaining a prediction coefficient meterMinimum prediction error power of autocorrelation coefficient 2-order prediction output by calculation unitAnd in the Euler-Watk Yule-Walker equation
If the minimum prediction error power*β<Coefficient of autocorrelationThe comparison unit outputs a signal to be coded to the linear prediction module;
if the minimum prediction error powerβ ≧ autocorrelation coefficientThe comparison unit outputs a signal to be coded to the statistical decision module;
where β is a parameter predetermined according to the signal characteristics.
Further, the statistical decision module includes a first decision unit, a second decision unit, and a parameter output unit, where the first decision unit receives a residual signal, maps the residual signal into a positive integer, and calculates a mean value mean; the second decision unit receives a signal to be coded, maps the signal to be coded into a positive integer and calculates a mean value mean;
the parameter output unit, if mean<m1If the parameter output unit outputs the selection parameter b ═ m12 to the Golomb coding module; if m1≤mean＜m2If the parameter output unit outputs the selection parameter b ═ m1To the Golomb coding module(ii) a If m2≤mean<m3If the parameter output unit outputs the selection parameter b ═ m2To the Golomb encoding module; if mean is greater than or equal to m3If the parameter output unit outputs the selection parameter b ═ m3To the Golomb encoding module.
Further, the linear prediction module includes a residual calculation unit, configured to obtain a prediction coefficient, predict a predicted value of the signal to be encoded based on P previous signals to be encoded, and calculate an interpolation between the digital data and the predicted value of the digital data to obtain a prediction residual.
Preferably, the special chip of the central processing unit takes the DSP as a main processor, takes the ARM chip as a main processor chip or takes the FPGA as the main processor.
The system further comprises an analog/digital signal input circuit, an analog/digital signal output circuit, a code stream output interface and a code stream input interface which are respectively connected with the central controller, wherein if the signal to be coded is an analog signal, the signal to be coded is input into the central processor after being subjected to AD conversion by the analog/digital signal input circuit, and if the signal to be coded is a digital signal, the signal to be coded is directly input into the central processor;
the Golomb coding module processes and generates a coding code stream, and the coding code stream is output through the coding code stream output interface; and the coding code stream is input into a central processing unit through a code stream input interface, is processed by the Golomb coding module to recover a signal to be coded, and if analog signals need to be recovered, the signal to be coded is output through an analog/digital signal output interface.
By the scheme, the invention at least has the following advantages:
the invention firstly preprocesses the signal and improves the operation speed. Through testing and analysis of the signal data, each frame of signal is selectively predicted in the linear prediction module. Linear prediction of order 2 is used.
And the linear prediction module selects different selection parameters to perform Golomb coding according to the digital characteristics of each frame of signal, so as to realize the self-adaptive performance of compression coding. Golomb codes different from Huffman codes are adopted in the lossless coding module for entropy coding, improvement can be carried out after testing, and the effectiveness and reliability of lossless compression coding are improved.
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 data frame is judged according to a judgment threshold, Golomb coding of self-adaptive selection parameters is adopted for a predicted frame and a non-predicted frame, flexibility is high, and 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.
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FIG. 1 is a flow diagram of the adaptive hybrid data lossless compression system of the present invention;
FIG. 2 is a block diagram of the architecture of the adaptive hybrid data lossless compression system 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 minimize the average code length of a positive integer data stream subject to geometric distribution, and can directly provide an optimal variable length code by using a shorter code for a smaller number and a longer code for a larger number.
The Golomb code of the positive integer n consists of a prefix code and a tail code
The prefix code is a unary codeword of q +1 bits, and
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 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 data frame with a large value, if the value b is 32, the value q is correspondingly large, the prefix code occupies a large number of bits, and the code length is large.
The present invention therefore also introduces a linear prediction technique to selectively predict all data frames. Namely, the data frame with smaller value is not predicted and is directly coded; and 2-order linear prediction is carried out on the data frame with a larger value, only prediction error coding is needed, and the bit number occupied by coding is reduced.
Example 1
Referring to fig. 1, an adaptive hybrid data lossless compression system according to a preferred embodiment of the present invention includes: the central controller comprises a statistical analysis module, a linear prediction module, a statistical decision module and a Golomb coding module;
the statistical analysis module compares the signal analysis statistical characteristics of the signal to be coded with a set threshold value to judge whether the signal to be coded needs to be predicted or not,
if prediction is needed, the statistical analysis module outputs a signal to be coded to the linear prediction module; the linear prediction module performs linear prediction on a signal to be coded to obtain a residual signal, and outputs the residual signal to the statistical decision module; the statistical decision module obtains a Golomb coding parameter based on the residual signal, outputs the Golomb coding parameter to the Golomb coding module, and the Golomb coding module codes the residual signal;
if the prediction is not needed, the statistical analysis module outputs a signal to be coded to the statistical judgment module; the statistical decision module obtains a Golomb coding parameter based on a signal to be coded, outputs the Golomb coding parameter to the Golomb coding module, and codes the signal to be coded by the Golomb coding module.
The central controller also comprises a prediction decision module, an inverse linear prediction module and a Golomb decoding module;
a prediction judgment module for judging whether the code stream is subjected to linear prediction,
if no linear prediction exists, the coding code stream is output to a Golomb decoding module, and a signal to be coded is recovered through processing of the Golomb decoding module;
if linear prediction is carried out, the coding code stream is output to an inverse linear prediction module for processing, then output to a Golomb decoding module, and a signal to be coded is recovered through processing of the Golomb decoding module.
The central controller is respectively connected with the analog/digital signal input circuit, the analog/digital signal output circuit, the code stream output interface and the code stream input interface, if the signal to be coded is an analog signal, the analog/digital signal is subjected to AD conversion by the analog/digital signal input circuit and then is input into the central processor, and if the signal to be coded is a signal to be coded, the signal to be coded is directly input into the central processor;
the Golomb coding module processes and generates a coding code stream, and the coding code stream is output through the coding code stream output interface; the code stream is input to a central processing unit through a code stream input interface, a signal to be coded is recovered through processing of the Golomb coding module, and the signal to be coded is output through an analog/digital signal output interface.
In the present embodiment, lossless compression is roughly realized by three parts. The first step is framing, which provides editable capabilities and is an important and essential feature of most signal compression algorithms to be encoded. Followed by intra-frame decorrelation. Redundant information contained in adjacent samples is removed through a linear predictor, and the linear predictor is selectively applied to frame data to generate a prediction error sequence. The parameters of the lossless coding predictor and the prediction error together represent the frame signal. And finally entropy coding. Entropy coding is lossless coding, and the redundancy of the error signal of the mountain side horse area is self. No information is lost in this process. The embodiment adopts Golomb code to carry out entropy coding on each frame of signal after analysis.
In practical application, a signal to be compressed is usually an analog signal, so that the analog signal needs to be converted into a digital signal through a/D sampling, the digital signal is compressed and encoded, and an encoded code stream is output.
Firstly, the signal is preprocessed, and the operation speed is improved. Through the test and analysis of the signal data, 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 data frame needs linear prediction or not according to the decision threshold.
If the data frame 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 signal, and then a residual signal is solved.
If the data frame does not need linear prediction, the data frame is directly mapped, data analysis is carried out on the mapped data, a proper parameter value b of Golomb coding is selected, and then Golomb coding is carried out. The data frame also contains two parts, 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.
In the adaptive hybrid data lossless compression system of the embodiment, if a data frame needs to be predicted, linear prediction is performed on a subsequent signal pair according to sampling values of first two sampling points of the signal, and then a residual signal is solved. 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 data frame is 1 bit, the first two sampling points are respectively coded by 16-bit binary, the linear prediction coefficients are respectively coded by 13-bit binary, and the selection parameter b has four conditions, so that two bit positions are occupied. The encoding of the data frame thus has two parts, namely the flag bit and the Golomb 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.
If the data frame does not need linear prediction, the data frame is directly mapped, data analysis is carried out on the mapped data, a proper parameter value b of Golomb coding is selected, and then Golomb coding is carried out. The data frame also contains two parts, 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.
In this embodiment, the decoding process is a process of decoding an input encoded code stream to recover a signal to be encoded. 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.
The decoding process is a process of decoding the input coded code stream to recover the signal to be coded. 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.
If the frame is a prediction frame, two prediction coefficients of 2-order prediction and 2 sampling point values and a selection coefficient b of Golomb coding need to be extracted, and then the coding code stream is decoded to recover a signal to be coded.
If the frame is not a prediction frame, only the selection coefficient b of the Golomb code needs to be extracted, and the code stream can be decoded to recover the signal to be coded.
Example 2
In the adaptive mixed lossless data compression system of this embodiment, based on embodiment 1, the linear prediction module includes a prediction coefficient calculation unit, and calculates a linear prediction coefficient of each frame of a signal to be encoded by operating a Levinson-Durbin algorithm, which specifically includes:
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。
The linear prediction module also comprises a comparison unit, and the prediction coefficient calculation unit outputs the autocorrelation coefficient in the Euler-Wolk Yule-Walker equation of the linear predictionMinimum prediction error power for 2 nd order predictionTo the result output unit;
the result output unit obtains the minimum prediction error power of the autocorrelation coefficient 2-order prediction output by the prediction coefficient calculation unitAnd in the Euler-Watk Yule-Walker equation
If the minimum prediction error power*β<Coefficient of autocorrelationThe comparison unit outputs a signal to be coded to the linear prediction module;
if the minimum prediction error powerβ ≧ autocorrelation coefficientThe comparison unit outputs a signal to be coded to the statistical decision module;
where β is a parameter predetermined according to the signal characteristics.
The self-adaptive module comprises a prediction residual calculation unit, and the specific calculation formula of the prediction residual (n) is as follows:
where s (n) is the data to be encoded,is the predicted value of the data to be encoded.
Prediction value of data to be encodedIs predicted by using the past p data s (n) to be coded, wherein
Wherein a isiiAre linear prediction coefficients.
The linear prediction in this embodiment selectively predicts all data frames. Namely, the data frame with smaller value is not predicted and is directly coded; and 2-order linear prediction is carried out on the data frame with a larger value, only prediction error coding is needed, and the bit number occupied by coding is reduced.
In this embodiment, the system further comprises an analog/digital signal input circuit, an analog/digital signal output circuit, a code stream output interface and a code stream input interface which are respectively connected with the central controller, if the signal to be coded is an analog signal, the signal to be coded is input into the central processor after being subjected to AD conversion by the analog/digital signal input circuit, and if the signal to be coded is a digital signal, the signal to be coded is directly input into the central processor;
the Golomb coding module processes and generates a coding code stream, and the coding code stream is output through the coding code stream output interface; and the coding code stream is input into a central processing unit through a code stream input interface, is processed by the Golomb coding module to recover a signal to be coded, and if analog signals need to be recovered, the signal to be coded is output through an analog/digital signal output interface.
In the foregoing embodiments, the statistical decision module includes a first decision unit, a second decision unit, and a parameter output unit, where the first decision unit receives a residual signal, maps the residual signal into a positive integer, and calculates a mean value mean; the second decision unit receives a signal to be coded, maps the signal to be coded into a positive integer and calculates a mean value mean;
the data mapping of the prediction residual to be coded of each frame by the first decision unit specifically comprises: replacing the band coding value c of the prediction residual to be coded of each frame with a mapping value d:
judging whether the band coding value c in the prediction residual error to be coded of each frame is less than 0,
if the band code value c is less than 0, the mapping value d is 2 c;
if the band encoding value c is greater than or equal to 0, the mapping value d is 2c + 1;
the second decision unit performs data mapping on the prediction residual to be coded of each frame, and specifically includes: replacing the band coding value c of the prediction residual to be coded of each frame with a mapping value d:
judging whether the band coding value c in the prediction residual error to be coded of each frame is less than 0,
if the band code value c is less than 0, the mapping value d is 2 c;
if the band encoding value c is greater than or equal to 0, the mapping value d is 2c + 1;
performing data mapping on each frame of signals to be coded, specifically comprising: replacing the band encoding value e of each frame of signal to be encoded with a mapping value f:
judging whether the band coding value e in the prediction residual error to be coded of each frame is less than 0,
if the band encoding value e is less than 0, the mapping value f is 2 e;
if the band encoding value e is equal to or greater than 0, the mapping value f is 2e + 1.
The parameter output unit is used for carrying out data analysis on a signal to be coded or a prediction residual error to obtain a mean value;
if prediction is needed, the parameter output unit obtains the selected parameter by calculating and analyzing the mean value of the residual signal output by the first decision unit,
if the residual signal mean<m1If the parameter output unit outputs the selection parameter b ═ m12 to the Golomb coding module;
if m1Residual signal mean ≦<m2If the parameter output unit outputs the selection parameter b ═ m1To the Golomb encoding module;
if m2Residual signal mean ≦<m3If the parameter output unit outputs the selection parameter b ═ m2To the Golomb encoding module;
if the residual signal mean is greater than or equal to m3If the parameter output unit outputs the selection parameter b ═ m3To the Golomb encoding module.
If the prediction is not needed, the parameter output unit obtains the selected parameter bar by calculating and analyzing the mean value of the signal to be coded output by the second decision unit,
if the signal mean to be encoded<m1If the parameter output unit outputs the selection parameter b ═ m12 to the Golomb coding module;
if m1Mean of the signal to be encoded is less than or equal to<m2If the parameter output unit outputs the selection parameter b ═ m1To the Golomb encoding module;
if m2Mean of the signal to be encoded is less than or equal to<m3If the parameter output unit outputs the selection parameter b ═ m2To the Golomb encoding module;
if the mean of the signal to be coded is greater than or equal to m3If the parameter output unit outputs the selection parameter b ═ m3To the Golomb encoding module.
m1、m2、m3And the value is determined according to the residual average value or the average value of the coded data.
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 (7)
1. An adaptive hybrid lossless data compression system, comprising: the central controller comprises a statistical analysis module, a linear prediction module, a statistical decision module and a Golomb coding module;
the statistical analysis module compares the signal analysis statistical characteristics of the signal to be coded with a set threshold value to judge whether the signal to be coded needs to be predicted or not,
if prediction is needed, the statistical analysis module outputs a signal to be coded to the linear prediction module; the linear prediction module performs linear prediction on a signal to be coded to obtain a residual signal, and outputs the residual signal to the statistical decision module; the statistical decision module obtains a Golomb coding parameter based on the residual signal, outputs the Golomb coding parameter to the Golomb coding module, and the Golomb coding module codes the residual signal;
if the prediction is not needed, the statistical analysis module outputs a signal to be coded to the statistical judgment module; the statistical decision module obtains a Golomb coding parameter based on a signal to be coded, outputs the Golomb coding parameter to the Golomb coding module, and codes the signal to be coded by the Golomb coding module.
2. The adaptive hybrid lossless data compression system of claim 1, wherein the central controller further comprises a prediction decision module, an inverse linear prediction module, a Golomb decoding module;
a prediction judgment module for judging whether the code stream is subjected to linear prediction,
if no linear prediction exists, the coding code stream is output to a Golomb decoding module, and a signal to be coded is recovered through processing of the Golomb decoding module;
if linear prediction is carried out, the coding code stream is output to an inverse linear prediction module for processing, then output to a Golomb decoding module, and a signal to be coded is recovered through processing of the Golomb decoding module.
3. The adaptive hybrid data lossless compression system according to claim 1, wherein the statistical analysis module includes a prediction coefficient calculation unit and a comparison unit, the prediction coefficient calculation unit is configured to run a levinson-durbin algorithm to solve a prediction coefficient of two-order linear prediction of the signal to be encoded and output an autocorrelation coefficient;
the comparison unit is used for obtaining the minimum prediction error power of the autocorrelation coefficient 2-order prediction output by the prediction coefficient calculation unitAnd in the Euler-Watk Yule-Walker equation
If the minimum prediction error powerThe comparison unit outputs a signal to be coded to the linear prediction module;
if the minimum prediction error powerThe comparison unit outputs a signal to be coded to the statistical decision module;
where β is a parameter predetermined according to the signal characteristics.
4. The adaptive hybrid data lossless compression system according to claim 1, wherein the statistical decision module includes a first decision unit, a second decision unit, and a parameter output unit, the first decision unit receives a residual signal, maps the residual signal to a positive integer, and calculates a mean; the second decision unit receives a signal to be coded, maps the signal to be coded into a positive integer and calculates a mean value mean;
the parameter output unit, if mean<m1If the parameter output unit outputs the selection parameter b ═ m12 to the Golomb coding module; if m1≤mean＜m2If the parameter output unit outputs the selection parameter b ═ m1To the Golomb encoding module; if m2≤mean<m3If the parameter output unit outputs the selection parameter b ═ m2To the Golomb encoding module; if mean is greater than or equal to m3If the parameter output unit outputs the selection parameter b ═ m3To the Golomb encoding module.
5. The adaptive hybrid lossless data compression system as claimed in claim 1, wherein the linear prediction module includes a residual calculation unit for obtaining prediction coefficients, predicting a predicted value of the signal to be encoded based on P preceding signals to be encoded, and calculating an interpolation of the digital data and the predicted value of the digital data to obtain a prediction residual.
6. The adaptive hybrid lossless data compression system of claim 1, wherein the dedicated chip of the central processing unit has a DSP as the main processor, an ARM chip as the main processor chip, or an FPGA as the main processor.
7. The adaptive hybrid data lossless compression system according to claim 2, further comprising an analog/digital signal input circuit, an analog/digital signal output circuit, a code stream output interface, and a code stream input interface, which are respectively connected to the central controller, wherein if the signal to be encoded is an analog signal, the signal is input to the central processor after being subjected to AD conversion by the analog/digital signal input circuit, and if the signal to be encoded is a digital signal, the signal is directly input to the central processor;
the Golomb coding module processes and generates a coding code stream, and the coding code stream is output through the coding code stream output interface; and the coding code stream is input into a central processing unit through a code stream input interface, is processed by the Golomb coding module to recover a signal to be coded, and if analog signals need to be recovered, the signal to be coded is output through an analog/digital signal output interface.
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| CN114003546B (en) * | 2022-01-04 | 2022-04-12 | 之江实验室 | Multi-channel switching value composite coding design method and device |
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| CN105915227B (en) | 2019-03-15 |
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