CN110265043B - Adaptive lossy or lossless audio compression and decompression calculation method - Google Patents
Adaptive lossy or lossless audio compression and decompression calculation method Download PDFInfo
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- G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
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- G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
- G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
Abstract
The invention relates to a self-adaptive lossy or lossless audio compression and decompression algorithm method, which mainly provides an audio compression algorithm method, and can compress audio data to a target bandwidth according to the requirements of environment and sound quality, thereby improving the compression efficiency, supporting fixed bandwidth, and ensuring that the compressed size of the original audio data of each block is consistent so as to be beneficial to the transmission between networks or wireless devices. And meanwhile, lossy and lossless audio compression is adopted in a self-adaptive mode, when each block is compressed, if the lossless compression result is judged to accord with the target bandwidth, the lossless audio compression is adopted, so that the sound quality completely consistent with the original audio can be achieved, and otherwise, the lossy audio compression is adopted.
Description
Technical Field
The present invention relates to an algorithm for adaptive lossy or lossless audio compression and decompression, and more particularly to an algorithm for audio compression to improve the compression efficiency and ensure the stability of audio transmission and sound quality.
Background
Audio file formats currently exist in many different types of file formats, but regardless of the format, the general method for obtaining audio data is: the audio voltage is sampled (quantized) at regular time intervals and the result is stored with some resolution, for example: CDDA (compact Disc Digital Audio) is 16 bits or 2 bytes per sample. Furthermore, there are different criteria for the time interval of the sampling, such as: the CDDA was used 44100 times per second, and the DVD was used 48000 or 96000 times per second. Therefore, the sampling frequency, the audio resolution and the number of audio channels (e.g. stereo is 2 channels) are the conditions for the audio file format, and the audio file format can support multiple encoding compression methods, which can be divided into two compression formats of audio files: lossless audio Compression (Lossless Compression) for example: WAV, FLAC, APE, ALAC, WavPack (WV); (two) Lossy audio Compression (Lossy Compression) such as: MP3, AAC, oggvbis, Opus.
The difference between the two is that the choice of lossless audio compression has the advantage if the original quality of the music is to be ensured: the decompressed data is identical to the uncompressed data and no loss in audio quality occurs. Although this approach can preserve the audio quality of the source file, it imposes a significant burden on real-time transmission, and therefore, for transmission compression between wireless devices, it is not likely to be the case.
In addition, the features of lossy audio compression: has a large compression ratio and provides good audio quality, thereby facilitating transmission between wireless devices. The lossy compressed audio file format is based on acoustic and psychological model operations, and can try to inhibit the loss of audio quality, so that the difference after compression is difficult to or cannot be perceived by common human ears, but if a high-level sound equipment is used for dialing and playing the audio compression format, the sound distortion is easy to hear.
Since the lossless audio compression rate is generally about 50% to 70% according to different types and contents of music, each original audio block (frame) is compressed to have different sizes and varies greatly. Compared with the lossy audio compression ratio which can be as low as 10% and can also maintain a certain audio quality, most of all, the size of each original audio block after compression is consistent, so that the lossy compression format is mostly adopted when a wireless or wired device transmits audio data.
However, in the existing audio compression method, taiwan patent publication no: TWI276047B shows a device and method for lossless audio compression/decompression entropy compression coding, the device includes a buffer, a time axis predictor and an entropy coder with a bit allocation, wherein the time axis predictor subtracts the predicted value of the input signal at the time point from the original input signal at the time point to generate a prediction error signal. Then, the prediction error signal is input to the entropy decoder assigned to the bit according to a coding rule, and is encoded into data intervals of different lengths. The entropy-compression-encoded data interval structure includes 32-bit header information, and the header information is followed by real data, but the data is substantially a difference between an original value of the data and an interval minimum value.
The calculation method uses Recursive Least Square (RLS) and Least Mean Square (LMS), and the calculation method has large calculation amount, slow convergence and different lengths of the divided coding intervals, namely, because the length of each coding interval is different and each coding interval needs 12-bit to represent the point length of the interval, thus causing the burden of real-time transmission of the wireless device.
Disclosure of Invention
In order to overcome the defects of the existing operation method, the invention mainly aims to adaptively adopt a loss (lossy) or lossless (lossless) compression method under a target bandwidth. And supports fixed bandwidth (fixed bit rate) to make the compressed size of the original audio data of each block consistent, so as to facilitate the transmission between networks or wireless devices.
Another objective of the present invention is to provide an audio compression algorithm method, which can improve the compression efficiency. The creation adopts lossy and lossless audio compression simultaneously, when each block is compressed, if the lossless compression result is judged to accord with the target bandwidth, the lossless audio compression is adopted if the lossless compression result accords with the target bandwidth, otherwise, the lossy audio compression is adopted. When the target bandwidth is gradually increased from low (200Kbps) to high (1000Kbps), the proportion of the blocks adopting lossless audio compression is gradually increased from 0% to 100%.
In order to achieve the purpose, the invention adopts the technical scheme that: a method for adaptive lossy or lossless audio compression and decompression algorithm, comprising: according to the original audio format condition and the environmental condition, cutting the audio data into blocks with fixed length, after the step of lossy audio compression is carried out, if the distortion is less than a certain value, carrying out lossless audio compression, otherwise, directly adopting the lossy audio compression; lossless audio compression is carried out when the distortion is smaller than a certain value, and the result is smaller than the target bandwidth value, if so, lossless audio compression is adopted; otherwise, adopting lossy audio compression; the lossy and lossless audio compression supports fixed bandwidth, and the size of the compressed original audio data of each block is consistent, so that the transmission between the network and the wireless device is facilitated; and when each block is compressed, judging whether the lossless compression result accords with the target bandwidth, adopting lossless audio compression, and otherwise, adopting lossy audio compression.
The present invention mainly provides a method for compressing audio data to a target bandwidth, such as 320Kbps, according to the environment and sound quality requirements when transmitting compressed audio over a wireless or wired network, so as to ensure stable transmission and sound quality. The larger the bandwidth, the better the sound quality is usually, while lossless compression requires the maximum bandwidth to achieve sound quality that is completely consistent with the original audio.
Drawings
FIG. 1 is a block diagram of an audio compression algorithm according to the present invention.
FIG. 2 is a block diagram of lossy audio compression according to the present invention.
FIG. 3 is a block diagram of the lossy audio decompression step according to the present invention.
FIG. 4 is a block diagram of lossless audio compression steps according to the present invention.
FIG. 5 is a block diagram of lossless audio decompression according to the present invention.
Wherein: the audio data is cut into blocks with fixed length, 1, lossy audio compression, 2, discrete cosine transformation, 21, quantization and 22 are carried out, compression and 23 are carried out, compared with target bandwidth, 24, scale factors are increased, 241, scale factors are decreased, 242, recording, 25, reading, 26, decompression, 27, quantization reduction, 28, inverse discrete cosine transformation and 29 are carried out, distortion is smaller than a certain value, 3, lossless audio compression and 4, first prediction operation and 41, second prediction operation and 42, compression and 43, recording, 44, reading, 45, decompression and 46, first prediction reduction operation and 47, second prediction reduction operation and 48 are carried out, all values and 49 are obtained, and the result is smaller than the target bandwidth value and 5.
Detailed Description
Referring to fig. 1, first, according to the original audio format condition and the existing environmental condition (bandwidth), the audio data is first cut into fixed length blocks 1 for lossy audio compression 2, which is beneficial for the transmission between the network and the wireless device.
Then, the lossy audio compression step is performed, with reference to fig. 2, step 1: discrete cosine transform 21, which performs Discrete Cosine Transform (DCT) on the audio data of the whole block to obtain a DCT coefficient (DCT coeffient); step 2: quantization 22, dividing all DCT coefficients by a scale factor (scale factor), and taking an integer to obtain a quantized (quantization) coefficient; and step 3: compressing 23, performing lossless compression on the quantization coefficient to obtain compressed data; and 4, step 4: comparing the compression result with the target bandwidth 24, if the compression result is larger than the target bandwidth, increasing the scale factor 241 and repeating the quantization 22 in the step 2 and the compression 23 in the step 3; if the bandwidth is smaller than the target bandwidth, the scale factor 242 is reduced, and the step 2 quantization 22 and the step 3 compression 23 are repeated; and finally, step 5: record 25 records the last compressed data of the block and the value of the scale factor.
After the step 2 of lossy audio compression, if the distortion is less than a certain value 3, performing 4 steps of lossless audio compression; otherwise, adopting a lossy audio compression step. Referring to fig. 3, the lossy audio decompression step, step 1: reading 26, reading the compressed data and scale factor (scale factor) of the block, step 2: decompressing 27, decompressing the compressed data to obtain the quantized coefficients, and then step 3: quantization reduction 28, which multiplies each quantization coefficient by a scale factor to approximate the original discrete cosine transform coefficient obtained in the lossy compression step 1; and finally, step 4: the Inverse Discrete cosine transform 29 performs Inverse Discrete cosine transform (Inverse Discrete cosine transform) to obtain audio data of the block, which is similar to the original audio data.
In the above, after the step of lossy audio compression 2, the distortion is smaller than a certain value 3, and then the lossless audio compression 4 is performed, referring to fig. 4,
step 1: a first prediction operation 41, which performs Linear Predictive Coding (LPC) on the audio data of the whole block to obtain a group of LPC coefficients (LPC coefficients), where the formula i:
y (n) is the nth predicted value, X (n-i) is the original value, Ai is the LPC coefficient, p is the number of LPC coefficients, p < ═ n, where the original value and LPC coefficients are integers.
Step 2: the second prediction operation 42 is to predict the error value E (n) equal to the original value X (n) minus the predicted value Y (n), and E (n) is usually much smaller than X (n), the formula two:
E(n)＝X(n)-Y(n)
and step 3: compressing 43, performing lossless compression on the error value to obtain compressed data; and finally, step 4: record 44 records p LPC coefficients and the previous p original values and the block and compressed data. This is a step of lossless audio compression 4, to achieve a result less than the target bandwidth value 5, if so, lossless audio compression is employed; otherwise, if not, the lossy audio compression is adopted.
However, with the lossless compression format, in the lossless decompression step, please refer to fig. 5, step 1: reading 45 read p LPC coefficients and block compression data; step 2: decompressing 46, decompressing the compressed data to obtain an error value; and step 3: performing a predictive reduction operation one 47, wherein a predicted value Y (p +1) can be obtained by using the first p original values and the LPC coefficient according to a formula one; and 4, step 4: a predictive reduction operation two (48) in which the original value X (p +1) is Y (p +1) + E (p +1) by formula two; finally, the predictive restoration operations one and two (47, 48) are repeated to obtain the full original value 49.
The following is an embodiment in which each block can be compressed losslessly by budgeting the audio data in advance to choose the compression rate.
The first embodiment is as follows: lossless compression step
1. A set of integers Xn is input, the absolute value is obtained by multiplying 2 and the sign is recorded in the lowest Bit (LSB) to obtain a set of positive integers Dn.
2. Calculating the respective bit lengths of the positive integers Dn to obtain a set of positive integers Bn.
3. The positive integer Dn and the positive integer Bn are divided into blocks of the same length, and the maximum bit length Bh is calculated for each block, where Br ═ Bh-K, K is a constant, typically 3 or 4.
4. Dividing Dn in each block into two parts, bit 0 to bit Br called remainder Rn, directly recording without compression, and adding bit Br +1 to bit Bh into the sequence Un to be compressed.
5. And performing entropy Encoding (entropy Encoding) compression operation on the Un to obtain compressed data Cn.
6. The compressed data Cn, the remainder Rn, and the maximum bit length Bh for each block are stored.
The first embodiment is as follows: lossless decompression step
1. The compressed data Cn, the remainder Rn, and the maximum bit length Bh in each block are read.
2. Decompressing the compressed data Cn to obtain the sequence Un to be compressed
3. Combining the sequence Un to be compressed and the remainder Rn into a positive integer Dn
4. Dividing the positive integer Dn by 2, adding the sign to obtain the integer Xn
Example two: lossless compression step
1. A set of integers Xn is input, the absolute value is obtained by multiplying 2, and the sign is recorded in the lowest bit (LSB), so that a set of positive integers Dn is obtained. (same step 1 of example one)
2. Calculating the respective bit lengths of the positive integers Dn to obtain a set of positive integers Bn. (same as step 2 of the first embodiment).
3. And carrying out linear predictive coding on the positive integer Bn to obtain a group of LPC coefficients (LPC coefficient).
4. And carrying out prediction operation by using a formula I to obtain a predicted value Pn.
5. Dividing a positive integer Dn into two parts, namely bit 0 to bit Pn, called remainder Rn, directly recording the remainder Rn without compression, adding the rest bits into a sequence Un to be compressed, and adding 0 if a predicted value Pn > is equal to a positive integer Bn.
6. And performing entropy Encoding (entropy Encoding) compression operation on the array Un to be compressed to obtain compressed data Cn.
7. The compressed data Cn is stored, with the remainder Rn following the LPC coefficients.
Example two: lossless decompression step
1. The compressed data Cn and LPC coefficients are read.
2. And carrying out entropy Encoding (entropy Encoding) decompression operation on the compressed data Cn to obtain a sequence Un to be compressed.
3. And performing prediction operation by using a formula I to obtain a predicted value Pn, and reading a remainder Rn according to the predicted value Pn.
4. And combining the sequence Un to be compressed and the remainder Rn into a positive integer Dn.
5. The positive integer Dn is divided by 2 and added with the sign to obtain the integer Xn.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the claims of the present invention; while the foregoing description is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
Claims (5)
1. A method for adaptive lossy or lossless audio compression and decompression algorithm, comprising:
according to the original audio format condition and the environmental condition, cutting the audio data into blocks with fixed length, after the step of lossy audio compression is carried out, if the distortion is less than a certain value, carrying out lossless audio compression, otherwise, directly adopting the lossy audio compression;
lossless audio compression is carried out when the distortion is smaller than a certain value, and the result is smaller than the target bandwidth value, if so, lossless audio compression is adopted; otherwise, adopting lossy audio compression;
the lossy and lossless audio compression supports fixed bandwidth, and the size of the compressed original audio data of each block is consistent, so that the transmission between the network and the wireless device is facilitated; and when each block is compressed, judging whether the lossless compression result accords with the target bandwidth, adopting lossless audio compression, and otherwise adopting lossy audio compression.
2. The adaptive lossy or lossless audio compression and decompression algorithm of claim 1, wherein the lossy audio compression characteristic is:
step 1: discrete cosine transform, which is to perform discrete cosine transform on the audio data of the whole block to obtain a coefficient;
step 2: quantizing, namely dividing all coefficients by a proportional factor, and obtaining quantized coefficients after taking integers;
and step 3: compressing, namely performing lossless compression on the quantization coefficient to obtain compressed data;
and 4, step 4: comparing the compression result with the target bandwidth, comparing the compression result,
if the bandwidth is larger than the target bandwidth, the scale factor is increased, quantization is repeated, and compression is carried out;
if the bandwidth is smaller than the target bandwidth, the scale factor is reduced, and the quantization is repeated and the compression is carried out;
and finally, step 5: recording the last compressed data of the block and the value of the scale factor.
3. The adaptive lossy or lossless audio compression and decompression algorithm of claim 1, wherein the lossless audio compression step is characterized by:
step 1: performing a first prediction operation, performing linear predictive coding on the audio data of the whole block to obtain a set of coefficients, wherein the formula one is as follows:
y (n) is the nth predicted value, X (n-i) is the original value, Ai is the LPC coefficient, p is the number of LPC coefficients, p < ═ n, wherein the original value and LPC coefficients are integers;
step 2: the second prediction operation, the error value E (n) is equal to the original value X (n) minus the predicted value Y (n), and E (n) is usually much smaller than X (n), the formula two:
E(n)＝X(n)-Y(n)
and step 3: compressing, namely performing lossless compression on the error value to obtain compressed data;
and finally, step 4: record p LPC coefficients and the first p original values and the block and compressed data.
4. The adaptive lossy or lossless audio compression and decompression algorithm of claim 3, wherein the lossless compression step is characterized by:
1. inputting a group of integers Xn, multiplying 2 to obtain an absolute value and recording the sign of the absolute value at the lowest bit to obtain a group of positive integers Dn;
2. calculating the individual bit length of the positive integer Dn to obtain a group of positive integers Bn;
3. dividing the positive integer Dn and the positive integer Bn into blocks with the same length, and calculating the maximum bit length Bh in each block, wherein Br is Bh-K, and K is a constant and is usually 3 or 4;
4. dividing Dn in each block into two parts, namely bit 0 to bit Br, called remainder Rn, directly recording the remainder Rn without compression, and adding bit Br +1 to bit Bh into a sequence Un to be compressed;
5. performing entropy coding compression operation on Un to obtain compressed data Cn;
6. the compressed data Cn, the remainder Rn, and the maximum bit length Bh for each block are stored.
5. The adaptive lossy or lossless audio compression and decompression algorithm of claim 3, wherein the lossless compression step is characterized by:
1. inputting a group of integers Xn, multiplying 2 to obtain an absolute value and recording the sign of the absolute value at the lowest bit to obtain a group of positive integers Dn;
2. calculating the individual bit length of the positive integer Dn to obtain a group of positive integers Bn;
3. carrying out linear predictive coding on the positive integer Bn to obtain a group of LPC coefficients;
4. performing prediction operation by using a formula I to obtain a predicted value Pn;
5. dividing a positive integer Dn into two parts, namely bit 0 to bit Pn, which are called remainder Rn, directly recording the remainder Rn without compression, adding the rest bits into a sequence Un to be compressed, and adding 0 if a predicted value Pn > is equal to a positive integer Bn;
6. performing entropy coding compression operation on the array Un to be compressed to obtain compressed data Cn;
7. the compressed data Cn is stored, with the remainder Rn following the LPC coefficients.
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