KR102229893B1 - Method for RF over IP Linear Predictive coding and Nonlinear Quantization Fused Compression Transmission - Google Patents

Abstract

A method and apparatus for RoIP linear prediction encoding and nonlinear quantization fusion compression transmission are provided. The RoIP linear prediction encoding and nonlinear quantization fusion compression transmission apparatus proposed in the present invention realizes compression by predicting the future signal sample value through the past signal sample value during the data transmission process and transmitting the error value of the predicted result. A block scaling unit that binds the error value output from the linear prediction unit to the blocks including a plurality of signal sample values along the time axis and inputs the data to the nonlinear quantization unit in the form of the block, and And a nonlinear quantization unit that determines a quantization level of a signal sample value and converts the signal sample value into a binary form.

Description

RoIP Linear Predictive Coding and Nonlinear Quantization Fused Compression Transmission {Method for RF over IP Linear Predictive Coding and Nonlinear Quantization Fused Compression Transmission}

The present invention relates to a method and apparatus for transmitting RoIP linear prediction encoding and nonlinear quantization fusion compression.

In a mobile communication base station, it is common to have an RF module that digitally modulates a voice signal or a data signal such as the Internet and transmits or receives it to a terminal and demodulates it. In recent years, in order to reduce the cost of a base station, wireless modem facilities are often installed in a base station controller (BSC) or a mobile switching center (MSC), rather than a wireless modem in a mobile communication base station. The base station reduces the price by allowing only the basic function of transmitting or receiving a simple RF signal. The base station has only a function of receiving an RF signal received from a terminal or transmitting an RF signal to the terminal.

The base station receives the RF signal from the terminal, and the binary data generated after sampling, quantizing, and compressing the signal at a sufficient rate are transmitted to the BSC or MSC through high-speed Internet Protocol (IP) data communication such as optical communication. do. Here, the reason for compressing the data is to convert the RF signal into a binary signal and restore it again so that the error should be lower than the reference value. The BSC or MSC receives it, restores the RF signal through the reverse process, demodulates it, and delivers it to the wired system of the mobile phone system. Conversely, when transmitting an RF signal to a terminal, the signal modulated by the BSC or MSC is sampled, quantized, and compressed, and then the binary data is transferred to the base station through the IP system, and the base station performs these reverse processes again to generate the RF signal. The base station transmits this to the terminal.

This simplifies the base station device, thereby reducing the cost of the base station device. Since the number of base station devices is much larger than that of BSC or MSC, the overall mobile communication installation cost is lowered. These systems are collectively referred to as RoIP (RF over Internet Protocol). It converts RF signals into IP data and transmits them.

Recently, the RoIP system is going to be applied to the cable broadcasting system as well. Standardization efforts are also underway in ITU-T. In general, cable broadcasting systems transmit downlink broadcasting signals in a broadband of almost 1GHz using RF signals through HFC (Hybrid Fiber Coax)-based cable broadcasting networks, and data generated from subscribers undergo digital modulation. Upstream RF signals are transmitted using 54MHz. In general, the noise problem does not appear seriously in downlink broadcast service transmission. However, in upstream data transmission, various noises, especially impulse or burst noises, generated in homes or industries, act as a great obstacle to upstream data transmission. Therefore, not all of the 5~54MHz band is available, but it is a reality that a fairly wide low frequency band cannot be used due to noise. In the cable broadcasting system that has been standardized in ITU-T recently, the existing HFC network is used for downlink communication and RoIP is applied to uplink communication. Upstream IP data communication uses FTTH (Fiber To The Home) installed in apartment houses or detached houses.

RoIP technology improves noise immunity and improves noise immunity by improving the signal quality deterioration problem, which is a disadvantage of RF signals according to distance due to digital signal attenuation. In addition, when applied to a base station such as mobile communication, cost reduction can be expected by simplifying the RF modulation and demodulation part in the base station device. However, in order to digitize the RF signal and transmit it as previous data, the data rate is higher than the pre-modulation data rate, and accordingly, wideband data transmission is required. For example, in the case of using the Quadrature Amplitude Modulation (QAM) method to transmit 10Mbps data, changing the RF signal to a binary code requires at least 4 times the bit rate.

In order to improve the wideband data transmission problem, an effective data compression method for reducing the bit rate of a digital signal is required. In particular, it can be seen that the compression efficiency is greatly deteriorated when the proposed technology is used in an environment in which burst noise or burst data generated by household noises such as washing machines, hair dryers, mixers, etc. are generated. The present invention proposes a method and an algorithm for improving the problems of the prior art.

The technical problem to be achieved by the present invention is to achieve a high compression rate when converting an RF signal into a binary digital signal, and impulse or burst noise that may occur in a home or industrial environment is an uplink data transmission process. To improve the effect on the communication quality during communication, to propose a method and apparatus capable of sufficiently compressing data in such a noisy environment while maintaining a high signal-to-noise ratio.

In one aspect, the RoIP linear prediction encoding and nonlinear quantization fusion compression transmission apparatus proposed in the present invention predicts a future signal sample value through a past signal sample value during a data transmission process and transmits an error value of the predicted result. Block scaling that combines the error values output from the linear prediction unit and the linear prediction unit into blocks including a plurality of signal sample values along the time axis and inputs the data to the nonlinear quantization unit in the form of the block. And a nonlinear quantization unit that determines a quantization level of a signal sample value in each block and converts the signal sample value into a binary form.

The linear prediction unit calculates the linear prediction coefficient of the signal to be predicted and stores it in the linear prediction filter, and the linear prediction filter adds the signal sample values in order from the front end of the filter through the pre-stored linear prediction coefficients to sample the signal. The value is predicted, and the error value of the predicted result is output by cyclically updating the signal sample value buffered in the linear prediction filter and predicting the future signal sample value.

The block scaling unit divides the error value output from the linear prediction unit into blocks according to predetermined time intervals in order to reduce the error by performing an appropriate quantization process according to the characteristics of the signal sample values in each block, and the signal samples in each block Specify the quantization level by scaling the amplitude range of.

The block scaling unit divides successive signals into blocks along the time axis and controls to reduce signal distortion caused by impulse or burst noise during a data transmission process.

The nonlinear quantization unit takes the value of the quantization level closest to the signal sample value in each block of the original signal matched to the quantization level through the block scaling unit, converts the corresponding level value into a binary form, and outputs it.

The RoIP linear prediction encoding and nonlinear quantization fusion compression transmission apparatus applies a RoIP system to a cable broadcasting system to perform data compression in uplink data transmission or white data transmission, and is applied to a mobile communication system to transmit and receive signals from a base station.

In another aspect, the RoIP linear prediction encoding and nonlinear quantization fusion compression transmission method proposed by the present invention predicts and predicts a future signal sample value using a past signal sample value during a data transmission process through a linear prediction unit. A step of realizing compression by transmitting an error value of .The error value output from the linear prediction unit through the block scaling unit is grouped into blocks including a plurality of signal sample values along a time axis, and the data is grouped into the block form. Inputting to a nonlinear quantization unit and determining a quantization level of a signal sample value in each block through the nonlinear quantization unit, and converting the signal sample value into a binary form.

According to embodiments of the present invention, when converting an RF signal into a binary digital signal, a high compression rate is achieved, and impulse or burst noise that may be generated in a home or industrial environment is prevented during the uplink data transmission process. By improving the effect on communication quality, data can be sufficiently compressed in such a noisy environment while maintaining a high signal-to-noise ratio. In addition, it can be used to provide efficient and high-quality communication at all stages of the signal transmission process, and is reliable using Linear Predictive Coding, Block Scaling, and Non-linear Quantization. By implementing an efficient compression method, it can be applied to a similar communication environment such as an uplink or downlink data link of a traditional mobile communication and RoIP system.

1 is a diagram illustrating a configuration of an apparatus for transmitting RoIP linear prediction encoding and nonlinear quantization fusion compression according to an embodiment of the present invention.
2 is a diagram illustrating a linear predictive coding algorithm according to an embodiment of the present invention.
3 is a flowchart illustrating a method of transmitting RoIP linear prediction encoding and nonlinear quantization fusion compression according to an embodiment of the present invention.
FIG. 4 is a diagram for comparing output waveforms of an apparatus for transmitting RoIP linear prediction encoding and nonlinear quantization fusion compression according to an embodiment of the present invention with the prior art.
5 is a diagram illustrating quantization matching values of nonlinear quantized samples according to an embodiment of the present invention.
6 is a diagram illustrating a process of converting a quantized sample value into a binary form according to an embodiment of the present invention.

In the present invention, recently, an RF over Internet Protocol (RoIP) system is also intended to be applied to a cable broadcasting system. The RoIP system converts RF signals into IP data and transmits them. According to an embodiment of the present invention, a high compression rate is achieved when converting an RF signal into a binary digital signal, and impulse or burst noise that may be generated in a home or industrial environment is prevented during the uplink data transmission process. The impact on communication quality can be improved. A method capable of sufficiently compressing data in such a noisy environment while maintaining a high signal-to-noise ratio is proposed. It is used to provide efficient and high-quality communication at all stages of the signal transmission process. The present invention implements a reliable and efficient compression method using Linear Predictive Coding, Block Scaling, and Non-linear Quantization. This method is applicable to traditional mobile communication and similar communication environments such as uplink or downlink data links of RoIP systems. Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram illustrating a configuration of an apparatus for transmitting RoIP linear prediction encoding and nonlinear quantization fusion compression according to an embodiment of the present invention.

The present invention relates to a method of maintaining a high signal-to-noise ratio with sufficient compression in a burst signal or noise environment. It is about a signal compression algorithm for providing efficient and high-quality communication functions at all stages of the signal transmission process.

The ultimate goal of the algorithm proposed in the present invention is to achieve two goals: data compression and noise immunity. The compression method is divided into three steps, and the first step is Linear Predictive Coding. Linear prediction coding is a process of realizing compression by predicting a future signal sample value through a signal sample value in the past during a data transmission process and transmitting an error value of the predicted result.

The second step is block scaling. Block scaling is a process in which an error value, which is an output of a linear prediction unit, is grouped into one block along a time axis, and data is input to a non-linear quantizer in the form of such a block. Since the original continuous signal sample values are divided into blocks in the block scaling step, signal distortion that may occur due to impulse or burst noise during the data transmission process is controlled to a minimum.

The final step is non-linear quantization. In this step, the nonlinear quantization unit processes each block individually, determines a quantization level suitable for a signal sample value in each block, and converts the signal sample value into a binary number.

The proposed RoIP linear prediction encoding and nonlinear quantization fusion compression transmission apparatus 100 includes a linear prediction unit 110, a block scaling unit 120, and a nonlinear quantization unit 130.

The linear prediction unit 110 predicts future signal sample values using past signal sample values during the data transmission process, and performs compression by transmitting an error value of the predicted result.

The linear prediction unit 110 receives the RF sample signal 111, calculates a linear prediction coefficient of the signal to be predicted, and stores it in the linear prediction filter, and the linear prediction filter is first applied to the filter through the linear prediction coefficient stored in advance. The error value of the predicted result by predicting the signal sample value by summing the signal sample value sequentially from the first step into which the signal sample value is entered, and cyclically updating the signal sample value buffered in the linear prediction filter to predict the future signal sample value. Output (112).

2 is a diagram illustrating a linear predictive coding algorithm according to an embodiment of the present invention.

In an embodiment of the present invention, a total of N signal samples are to be transmitted in the linear prediction coding process of the linear prediction unit 110. First, before starting the linear prediction process, a linear prediction coefficient, which is a characteristic of a signal to be predicted, is calculated and stored in the linear prediction filter 210. The representative linear prediction coding function is represented by the following equation:

은 선형 예측부(110)에서 출력한 예측오차 값이고,

은 선형 예측 필터에 입력한 오리지널 신호이다. 선형 예측 계수를 계산하는 방법은 일반적으로 수식 1에 입력 신호 값을 대입하여 행렬식을 만든 다음, 레빈슨-더빈(Levinson-Durbin) 알고리즘 등을 통하여 이러한 행렬식의 해인 선형 예측 계수(

)를 구한다.

는 선형 예측 필터 안에 저장되어 있는 선형 예측 계수이며, 이와 필터에 입력한 0 번째부터 p-1 번째까지 신호 샘플을 곱하여 결과값을 합산하여 후속 입력한 p 번째 오리지널 신호간의 차이값을 오차신호로 출력한다.

Is the prediction error value output from the linear prediction unit 110,

Is the original signal input to the linear prediction filter. In general, the method of calculating the linear prediction coefficient is to create a determinant by substituting the input signal value into Equation 1, and then using the Levinson-Durbin algorithm, the solution of the determinant is the linear prediction coefficient (

).

Is the linear prediction coefficient stored in the linear prediction filter, and the result is multiplied by the signal samples from 0th to p-1th input to the filter, and the result is summed, and the difference value between the p-th original signal input subsequently is output as an error signal. do.

The linear prediction filter 210 predicts the p-th signal sample value by summing the signal sample values from the 0th to the p-1th in front of the linear prediction filter 210 first through the linear prediction coefficients stored in advance. Start the prediction process. The prediction result of the p-th signal sample value is compared with the actually collected p-th signal sample value to output an error value, and the 0-th signal sample value is removed in the linear prediction filter 210, and the p-th signal sample value is received. From now on, while cyclically updating the signal sample values buffered in the linear prediction filter 210, a prediction error value of the entire signal is output in a manner that predicts future signal sample values. In the signal output stage, the signal sample values from 0 to p-1 are transmitted as the original values so that the receiver can predict the signal sample values from the p-th to the N-th in the same manner, and the output result is expressed with the prediction error from the p-th. do.

Referring back to FIG. 1, the block scaling unit 120 combines the error value output from the linear prediction unit 110 into blocks including a plurality of signal sample values along the time axis, and collects data in the block form, In other words, the output 121 in the form of a divided sample signal block is input to the nonlinear quantization unit 130.

The block scaling unit 120 divides the error values output from the linear prediction unit into blocks according to predetermined time intervals in order to reduce errors by performing an appropriate quantization process according to the characteristics of the signal sample values in each block. The quantization level is specified by scaling the amplitude range of the signal samples in the block. It is possible to control to reduce signal distortion caused by impulse or burst noise during a data transmission process by dividing successive signals into blocks along a time axis.

The block scaling unit 120 divides the output of the linear prediction unit 110 at regular time intervals on the time axis and scales the amplitude range of signal samples in each block to designate a quantization level. According to an embodiment of the present invention, each block may include 32, 64, 128 or more time samples depending on the hardware buffer capability. After dividing the input signal into a block shape, the sampled signal included in each block is analyzed to match the maximum signal value of each block with the maximum quantization level of the nonlinear quantization unit.

The nonlinear quantization unit 130 determines a quantization level of a signal sample value in each block and converts the signal sample value into a binary form. The nonlinear quantization unit 130 takes the original signal matched to the quantization level through the block scaling unit, takes a quantization level value closest to the signal sample value in each block, converts the corresponding level value into a binary form, and outputs it. In other words, the quantized signal is output 131 in a binary form.

In the quantization step, nonlinear quantization is used. Since the probability distribution of the signal amplitude is generally non-uniform, the probability of occurrence of a small signal is much greater than that of a large signal. Nonlinear quantization provides more quantization levels in a small signal range and less quantization levels in a large signal range. Therefore, compared to linear quantization, the nonlinear quantization system can more accurately reconstruct the original signal, so the coded signal carries the largest information. The original signal is matched to the corresponding quantization level through block scaling, and the value of the quantization level closest to the samples in the block is taken, and the level value is converted into binary form and output.

As described above, the proposed RoIP linear prediction coding and nonlinear quantization fusion compression transmission device can perform data compression in uplink data transmission or white data transmission by applying the RoIP system to a cable broadcasting system, and is applied to a mobile communication system to provide a base station. It can send and receive signals from.

3 is a flowchart illustrating a method of transmitting RoIP linear prediction encoding and nonlinear quantization fusion compression according to an embodiment of the present invention.

The RoIP linear prediction encoding and nonlinear quantization fusion compression transmission method realizes compression by predicting the future signal sample value through the past signal sample value during the data transmission process and transmitting the error value of the predicted result through a linear prediction unit. Step 310, grouping the error value output from the linear prediction unit through the block scaling unit into blocks including a plurality of signal sample values along the time axis, and inputting the data to the nonlinear quantization unit in the form of the block (320). ) And determining a quantization level of a signal sample value in each block through a nonlinear quantization unit, and converting the signal sample value into a binary form (330).

In step 310, compression is realized by predicting future signal sample values using past signal sample values during the data transmission process through a linear prediction unit and transmitting an error value of the predicted result.

In step 310, the linear prediction unit receives the RF sample signal, calculates a linear prediction coefficient of the signal to be predicted, and stores it in the linear prediction filter, and the linear prediction filter first enters the filter through the previously stored linear prediction coefficient. The signal sample value is predicted by summing the signal sample values in order from the previous stage, and the error value of the predicted result is calculated by cyclically updating the signal sample values buffered in the linear prediction filter and predicting the future signal sample values. Print it out.

In step 320, the error value output from the linear prediction unit through the block scaling unit is grouped into blocks including a plurality of signal sample values along a time axis, and data is input to the nonlinear quantization unit in the form of the block.

In step 320, the block scaling unit divides the error value output from the linear prediction unit into blocks according to predetermined time intervals in order to reduce the error by performing an appropriate quantization process according to the characteristics of the signal sample values in each block. And scale the amplitude range of the signal samples in each block to specify the quantization level. It is possible to control to reduce signal distortion caused by impulse or burst noise during a data transmission process by dividing successive signals into blocks along a time axis.

The block scaling unit divides the output of the linear prediction unit at regular time intervals on the time axis and scales the amplitude range of signal samples in each block to designate a quantization level. According to an embodiment of the present invention, each block may include 32, 64, 128 or more time samples depending on the hardware buffer capability. After dividing the input signal into a block shape, the sampled signal included in each block is analyzed to match the maximum signal value of each block with the maximum quantization level of the nonlinear quantization unit.

In step 330, a quantization level of a signal sample value in each block is determined through a nonlinear quantization unit, and the signal sample value is converted into a binary form.

In step 330, the original signal matched to the quantization level through the block scaling unit is obtained by taking the value of the quantization level closest to the signal sample value in each block, and converting the corresponding level value into a binary form and outputting it. In other words, the quantized signal is output in binary form.

In the quantization step, nonlinear quantization is used. Since the probability distribution of the signal amplitude is generally non-uniform, the probability of occurrence of a small signal is much greater than that of a large signal. Nonlinear quantization provides more quantization levels in a small signal range and less quantization levels in a large signal range. Therefore, compared to linear quantization, the nonlinear quantization system can more accurately reconstruct the original signal, so the coded signal carries the largest information. The original signal is matched to the corresponding quantization level through block scaling, and the value of the quantization level closest to the samples in the block is taken, and the level value is converted into binary form and output.

FIG. 4 is a diagram for comparing output waveforms of an apparatus for transmitting RoIP linear prediction encoding and nonlinear quantization fusion compression according to an embodiment of the present invention with the prior art.

In an embodiment of the present invention, FIG. 4A is a time domain data block including 64 signal samples. The normal original signal amplitude is between -0.6 and 0.6 and increases from the 50th sample to a range of -3 to 3 due to the influence of impulse noise.

When the quantization level of the signal samples is divided in a general manner without block scaling as shown in FIG. 4B, all 64 samples use one quantization level indicated by a horizontal line in FIG. 4B.

In contrast, when using the RoIP linear prediction coding and nonlinear quantization fusion compression transmission method proposed in the present invention, as shown in FIG. Different quantization levels are used. Because the amplitude of the 32 signal samples on the left is small, they can use more quantization levels than the case on the right within a narrower range. An error occurring in the quantization process may be reduced by lowering the maximum quantization level according to the characteristics of the sample in a section with a signal level lower than that of a method in which all samples of FIG. 4B use a unique quantization level.

5 is a diagram illustrating quantization matching values of nonlinear quantized samples according to an embodiment of the present invention.

6 is a diagram illustrating a process of converting a quantized sample value into a binary form according to an embodiment of the present invention.

5 is an example of quantization level distribution for 32 signal samples. In this example, the original signal is already matched to 128 quantization levels through block scaling, and samples in the block take values of the nearest quantization level, and the corresponding level values as shown in FIG. 6 are converted into binary form and output.

As described above, there is provided a method of maintaining a high signal-to-noise ratio while sufficiently compressing in a burst signal or noise environment through an effective data compression method according to an embodiment of the present invention.

In the simulation of the present invention, the effects of the proposed RoIP linear prediction coding and nonlinear quantization fusion compression transmission method were tested by adding very severe impulse noise with zero SNR to the upstream signal of the RoIP system. According to the experimental results, it was confirmed that when a compression ratio of 50% or more was achieved in this environment and the original signal was restored, the error vector value can be controlled to less than about 3% (about 7% when block scaling is not applied). The present invention can be applied to similar communication devices such as mobile communication base stations and cable broadcasting transmission/reception as well as downlink data transmission on the RoIP system. In particular, RoIP technology overcomes the requirement of high bit rate caused by the digitization of RF signals, and minimizes signal distortion problems caused by burst noise that occurs in household environments, that is, burst noise generated by washing machines, hair dryers, and mixers. I can.

The apparatus described above may be implemented as a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the devices and components described in the embodiments include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It may be implemented using one or more general purpose or special purpose computers, such as a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications executed on the operating system. Further, the processing device may access, store, manipulate, process, and generate data in response to the execution of software. For the convenience of understanding, although it is sometimes described that one processing device is used, one of ordinary skill in the art, the processing device is a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of these, configuring the processing unit to operate as desired or processed independently or collectively. You can command the device. Software and/or data may be interpreted by a processing device or, to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. Can be embodyed. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -A hardware device specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

As described above, although the embodiments have been described by the limited embodiments and drawings, various modifications and variations are possible from the above description to those of ordinary skill in the art. For example, the described techniques are performed in a different order from the described method, and/or components such as systems, structures, devices, circuits, etc. described are combined or combined in a form different from the described method, or other components Alternatively, even if substituted or substituted by an equivalent, an appropriate result can be achieved.

Therefore, other implementations, other embodiments, and those equivalent to the claims also fall within the scope of the claims to be described later.

Claims (11)

A linear prediction unit for realizing compression by predicting a future signal sample value through a signal sample value in the past during a data transmission process and transmitting an error value of the predicted result;
A block scaling unit for grouping an error value output from the linear prediction unit into blocks including a plurality of signal sample values along a time axis and inputting data to the nonlinear quantization unit in the form of the block; And
A nonlinear quantization unit that determines the quantization level of signal sample values in each block and converts the signal sample values into binary form.
RoIP linear prediction encoding and nonlinear quantization fusion compression transmission device comprising a. The method of claim 1,
The linear prediction unit,
The linear prediction coefficient of the signal to be predicted is calculated and stored in the linear prediction filter, and the linear prediction filter predicts the signal sample value by summing the signal sample values in order from the front end of the filter through the pre-stored linear prediction coefficients. The error value of the predicted result is output by cyclically updating the signal sample value buffered in the linear prediction filter and predicting the future signal sample value.
RoIP linear prediction coding and nonlinear quantization fusion compression transmission device. The method of claim 1,
The block scaling unit,
In order to reduce the error by performing an appropriate quantization process according to the characteristics of the signal sample values in each block, the error value output from the linear prediction unit is divided into blocks according to predetermined time intervals, and the amplitude range of the signal samples in each block To specify the quantization level by scaling
RoIP linear prediction coding and nonlinear quantization fusion compression transmission device. The method of claim 1,
The block scaling unit,
Controls to reduce signal distortion caused by impulse or burst noise during data transmission by dividing successive signals into blocks along the time axis.
RoIP linear prediction coding and nonlinear quantization fusion compression transmission device. The method of claim 1,
The nonlinear quantization unit,
The original signal matched to the quantization level through the block scaling unit takes the value of the quantization level closest to the signal sample value in each block, converts the corresponding level value into binary form, and outputs it.
RoIP linear prediction coding and nonlinear quantization fusion compression transmission device. delete Performing compression by predicting a future signal sample value using a past signal sample value during a data transmission process through a linear prediction unit and transmitting an error value of the predicted result;
Grouping an error value output from the linear prediction unit through a block scaling unit into blocks including a plurality of signal sample values along a time axis and inputting data to a nonlinear quantization unit in the form of the block; And
Determining a quantization level of a signal sample value in each block through a nonlinear quantization unit and converting the signal sample value into a binary form
RoIP linear prediction coding and nonlinear quantization fusion compression transmission method comprising a. The method of claim 7,
The step of realizing compression by predicting a future signal sample value using a past signal sample value during the data transmission process through a linear prediction unit and transmitting an error value of the predicted result,
The linear prediction coefficient of the signal to be predicted is calculated and stored in the linear prediction filter, and the linear prediction filter predicts the signal sample value by summing the signal sample values in order from the front end of the filter through the pre-stored linear prediction coefficients. The error value of the predicted result is output by cyclically updating the signal sample value buffered in the linear prediction filter and predicting the future signal sample value.
RoIP linear prediction coding and nonlinear quantization fusion compression transmission method. The method of claim 7,
Grouping the error value output from the linear prediction unit through the block scaling unit into blocks including a plurality of signal sample values along a time axis and inputting data to the nonlinear quantization unit in the form of the block,
In order to reduce the error by performing an appropriate quantization process according to the characteristics of the signal sample values in each block, the error value output from the linear prediction unit is divided into blocks according to predetermined time intervals, and the amplitude range of the signal samples in each block To specify the quantization level by scaling
RoIP linear prediction coding and nonlinear quantization fusion compression transmission method. The method of claim 7,
Grouping the error value output from the linear prediction unit through the block scaling unit into blocks including a plurality of signal sample values along a time axis and inputting data to the nonlinear quantization unit in the form of the block,
Controls to reduce signal distortion caused by impulse or burst noise during data transmission by dividing successive signals into blocks along the time axis.
RoIP linear prediction coding and nonlinear quantization fusion compression transmission method. The method of claim 7,
Determining a quantization level of a signal sample value in each block through a nonlinear quantization unit and converting the signal sample value into a binary form,
The original signal matched to the quantization level through the block scaling unit takes the value of the quantization level closest to the signal sample value in each block, converts the corresponding level value into binary form, and outputs it.
RoIP linear prediction coding and nonlinear quantization fusion compression transmission method.

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