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An acceleration method to perform MPEG Audio Layer III compression with DSP

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An acceleration method to perform MPEG Audio Layer III compression with DSP

【Summary】MPEG audio layer III compression algorithm is a high fidelity and efficient compression coding algorithm specified by ISO11172-3 standard.

Due to the high complexity of the Layer III compression algorithm and the large amount of computation, a speedup measure is proposed to implement the key operations of the Layer III compression algorithm based on a Digital Signal Processor (DSP) in applications in real time. 【Key Words】Huffman MPEG DSP Compression Coding 1 Overview Digital audio compression technology provides people with greater

【Summary】MPEG Audio Layer III compression algorithm is a high-fidelity and efficient compression coding algorithm specified by the ISO11172-3 standard. Due to the high complexity of the Layer III compression algorithm and the large amount of computation, a speedup measure is proposed to implement the key operations of the Layer III compression algorithm based on a Digital Signal Processor (DSP) in applications in real time.
【Key Words】 DSP MPEG Huffman Compression Coding
1. General Information

Digital audio compression technology provides people with a more efficient method of transmitting and storing audio. There are many techniques for audio compression, and their complexity, audio compression quality, and compression ratio vary greatly. Such as: μ-law audio compression algorithm, its features are simple, but the compression ratio is very low, but the sound quality is average. According to CCITT G. 711 suggested that the natural log quantization process can provide relatively high precision quantization when the input amplitude is relatively small, while for large-scale signals with a relatively small probability of occurrence, the quantization noise it is relatively large. This quantization method makes the 8-bit digital quantization signal equivalent to 14-bit linear quantization in terms of quantization noise. ADPCM compression encoding takes full advantage of the relatively small amplitude variation characteristics of adjacent sample values, and the output result of the encoding is the difference between the current sample value and the predicted value. Although the fidelity of ADPCM encoding is high, its compression ratio is relatively small, and it can only reach a compression ratio of 4/1. The improved ADPCM encoding method includes the improved algorithm proposed by IMA (Interactive Multimedia Association), G. CCITT’s G. 721, g. 723 recommendations, etc

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Audio compression, how it works Part 4

Other divisions of compression methods.

In the field of audio compression, there are two compression methods, lossy compression and lossless compression. Commonly seen MP3, WMA, OGG are called lossy compression As the name suggests, lossy compression reduces the audio sample rate and bit rate, and the output audio file will be smaller than the original file. . Another audio compression is called lossless compression, which is what we’re talking about. Lossless compression can compress the volume of the audio file to a smaller size on the premise of saving 100% of all the data in the original file, and after restoring the compressed audio file, it can achieve the same size and same bitrate as the source file. Lossless compression formats include APE, FLAC, WavPack, LPAC, WMALossless, AppleLossless, La, OptimFROG, Shorten, while common and conventional lossless compression formats are just APE and FLAC. [1]
Main classifications and typical representatives of audio compression algorithms.edit streaming
Generally speaking, audio compression techniques can be divided into two categories: lossless compression and lossy compression, and according to different compression schemes, they can be divided into time-domain compression, transform compression, and time-domain compression. subband, as well as hybrid compression in which multiple technologies are combined with each other. Various compression techniques have large differences in algorithm complexity (including time complexity and space complexity), audio quality, algorithm efficiency (ie compression ratio), and codec delay. The applications of various compression techniques are also different.
Time domain compression technology (or waveform coding)
It directly processes the sample values of the audio PCM code stream and compresses the code stream through silence detection, nonlinear quantization, and difference. Common features of this type of compression technology are low algorithm complexity, average sound quality, small compression ratio (CD quality > 400kbps), and shortest codec delay (relative to other technologies) . This type of compression technology is generally used for voice compression and low bitrate (small source signal bandwidth) applications. Time domain compression technology mainly includes G.711, ADPCM, LPC, CELP, and block compression technology developed on these technologies, such as NICAM, Subband ADPCM (SB-ADPCM) technology.
Subband compression technology
Subband coding theory was first proposed by Crochiere et al. in 1976. The basic idea is to decompose the signal into the sum of components into several subbands and then adopt different compression strategies for each subband component according to its different layout features to reduce code rate. The usual subband compression technology and transform compression technology described below are based on the human perception model (psychoacoustic model) of the sound signal, and the quantization order of the subband samples or the samples The frequency domain is determined by analyzing the spectrum of the signal. other parameters are selected, so it can also be called perceptual compression encoding (Perceptual). Compared with time domain compression technology, these two compression methods are much more complicated. At the same time, the coding efficiency and sound quality are also greatly improved, and the coding delay is correspondingly increased. Generally speaking, the complexity of subband coding is slightly less than that of transform coding and the coding delay is relatively short.

Audio compression, how it works Part 3

Compression encoding method

According to different compression principles, audio signal coding is divided into waveform coding, parameter coding, and coding forms that integrate various technologies.
(1) Waveform coding directly samples the time-domain or frequency-domain waveform of the audio signal at a certain rate, and then quantizes the amplitude samples hierarchically, transforms them into digital codes, and outputs a signal coding system reconstructed from the waveform data. , the waveform is as consistent as possible with the original sound waveform, preserving detailed signal changes and various transition characteristics.
(2) Parametric coding First, a feature model based on different signal sources, such as language signals, natural sounds, etc., is established through feature parameter extraction and coding processing, trying to that the reconstructed sound signal is as loud as possible. to keep the semantics of the original sound, but reconstructed. The waveform of the signal may be quite different from the waveform of the original sound signal. Characteristic parameters in common use include formant, linear prediction coefficient, frequency band division filter and other parameter encoding techniques, which can realize low-speed sound signal encoding, and the bit rate can be compressed at 2 Kbit/s – 4.8 Kbit/s, but the sound quality can only reach Moderate, especially the low degree of naturalness, only suitable for language transmission and expression.
(3) Hybrid coding The coding way that combines waveform coding and parameter coding overcomes the weaknesses of original waveform coding and parameter coding, and strives to maintain high quality of coding of waveforms and the low rate parameter coding, at a rate of 4 -16Kbit/s A high quality synthetic sound signal can be obtained. The basis of hybrid coding is linear predictive coding (LPC), commonly used coding methods such as pulse-excited linear prediction coding (MPLPC), planned pulse-excited linear prediction coding (KPELPC), predictive coding Codebook Excited Linear (CELPC), etc.

Compression encoding method Part 2

Other divisions of compression methods

In the field of audio compression, there are two compression methods, lossy compression and lossless compression. Commonly seen MP3, WMA, OGG are called lossy compression As the name suggests, lossy compression reduces the audio sample rate and bit rate, and the output audio file will be smaller than the original file. . Another audio compression is called lossless compression, which is what we’re talking about. Lossless compression can compress the volume of the audio file to a smaller size on the premise of saving 100% of all the data in the original file, and after restoring the compressed audio file, it can achieve the same size and same bitrate as the source file. Lossless compression formats include APE, FLAC, WavPack, LPAC, WMALossless, AppleLossless, La, OptimFROG, Shorten, while common and conventional lossless compression formats are just APE and FLAC. [1]
Main classifications and typical representatives of audio compression algorithms.edit streaming
Generally speaking, audio compression techniques can be divided into two categories: lossless compression and lossy compression, and according to different compression schemes, they can be divided into time-domain compression, transform compression, and time-domain compression. subband, as well as hybrid compression in which multiple technologies are combined with each other. Various compression techniques have large differences in algorithm complexity (including time complexity and space complexity), audio quality, algorithm efficiency (ie compression ratio), and codec delay. The applications of various compression techniques are also different.
Time domain compression technology (or waveform coding)
It directly processes the sample values of the audio PCM code stream and compresses the code stream through silence detection, nonlinear quantization, and difference. Common features of this type of compression technology are low algorithm complexity, average sound quality, small compression ratio (CD quality > 400kbps), and shortest codec delay (relative to other technologies) . This type of compression technology is generally used for voice compression, low bit rate (small source signal bandwidth) applications. Time domain compression technology mainly includes G.711, ADPCM, LPC, CELP, and block compression technology developed on these technologies, such as NICAM, Subband ADPCM (SB-ADPCM) technology.
Subband compression technology
Subband coding theory was first proposed by Crochiere et al. in 1976. The basic idea is to decompose the signal into the sum of components into several subbands and then adopt different compression strategies for each subband component according to its different layout features to reduce code rate. The usual subband compression technology and transform compression technology described below are based on the human perception model (psychoacoustic model) of the sound signal, and the quantization order of the subband samples or the samples The frequency domain is determined by analyzing the spectrum of the signal. other parameters are selected, so it can also be called perceptual compression encoding (Perceptual). Compared with time domain compression technology, these two compression methods are much more complicated. At the same time, the coding efficiency and sound quality are also greatly improved, and the coding delay is correspondingly increased. Generally speaking, the complexity of subband coding is slightly less than that of transform coding and the coding delay is relatively short.

Compression encoding method

Transmission

According to different compression principles, audio signal coding is divided into waveform coding, parameter coding, and coding forms that integrate various technologies.
(1) Waveform coding directly samples the time-domain or frequency-domain waveform of the audio signal at a certain rate, and then quantizes the amplitude samples hierarchically, transforms them into digital codes, and outputs a signal coding system reconstructed from the waveform data. , the waveform is as consistent as possible with the original sound waveform, preserving detailed signal changes and various transition characteristics.
(2) Parametric coding First, a feature model based on different signal sources, such as language signals, natural sounds, etc., is established through feature parameter extraction and coding processing, trying to that the reconstructed sound signal is as loud as possible. to keep the semantics of the original sound, but reconstructed. The waveform of the signal may be quite different from the waveform of the original sound signal. Characteristic parameters in common use are formant, linear prediction coefficient, frequency band division filter and other parameter coding technologies, which can realize low-speed sound signal coding, and bit rate. can be compressed to 2 Kbit/s – 4.8 Kbit/s, but the sound quality can only reach moderate naturalness, especially low, only suitable for language transmission and expression.
(3) Hybrid coding The coding way that combines waveform coding and parameter coding overcomes the weaknesses of original waveform coding and parameter coding, and strives to maintain high quality of coding of waveforms and the low rate parameter coding, at a rate of 4 -16Kbit/s A high quality synthetic sound signal can be obtained. The basis of hybrid coding is linear predictive coding (LPC), commonly used coding methods such as pulse-excited linear prediction coding (MPLPC), scheduling pulse-excited linear prediction coding (KPELPC), Codebook Excited Linear Prediction (CELPC), etc.

Audio compression, how it works Part 2

Redundant information for transmission signals

Digital audio compression coding compresses the audio data signal as much as possible on the premise of ensuring that the signal is not audibly distorted. Digital audio compression coding is implemented by removing redundant components in sound signals. So-called redundant components refer to signals in the audio that cannot be perceived by the human ear and do not help determine the timbre, pitch, and other information of the sound. Redundant signals include audio signals outside the range of human hearing and masked audio signals. For example, the frequency range of the sound signal that can be perceived by the human ear is 20 Hz to 20 KHz, and frequencies other than this frequency that cannot be detected by the human ear can be considered as redundant signals. In addition, according to the physiological and psychoacoustic phenomena of the human ear, when a strong signal and a weak signal exist at the same time, the weak signal will be masked by the strong signal and cannot be heard, so the weak signal can be regarded as a redundant signal. Do not send. This is the masking effect of human hearing, which is mainly manifested in the spectral masking effect and the time-domain masking effect, which are presented below:
Spectral masking effects.
After the sound energy of a frequency is below a certain threshold, it will not be heard by the human ear, and this threshold is called the minimum audible threshold. When another sound with higher energy appears, the threshold value close to the frequency of the sound will increase considerably, which is known as the masking effect.

Masking effects in the time domain.
When strong and weak signals appear at the same time, there is also a masking effect in the time domain. That is, when the two occur very close in time, the masking effect will also occur. Time-domain masking is divided into three parts: pre-masking, simultaneous masking, and post-masking. Pre-masking refers to the short time before the human ear hears a strong signal, the already existing weak signal will be masked and cannot be heard. Simultaneous masking means that when a strong signal and a weak signal exist at the same time, the weak signal is masked by the strong signal and cannot be heard. Post-masking means that when the strong signal disappears, it takes a long period of time to hear the weak signal again, which is called post-masking. These weak masked signals can be considered redundant signals.

Audio compression, how it works

audio compression

It must have a corresponding inverse transform, called decompression or decoding. The audio signal can introduce a lot of noise and some distortion after passing through a codec system

Audio compression technology refers to the application of suitable digital signal processing technology to the original digital audio signal stream (PCM encoding), without losing the amount of useful information, or under the condition that the loss introduced insignificant, reducing (compressing) its code rate, and also called compression encoding. It must have a corresponding inverse transform, called decompression or decoding. Audio signals can introduce a great deal of noise and some distortion after passing through a codec system. The advantages of digital signal are obvious, but it also has its own corresponding disadvantages, ie increased storage capacity requirements and increased channel capacity requirements during transmission. Taking a CD as an example, the sampling frequency is 44.1KHz and the quantization precision is 16 bits, so a stereo audio signal for 1 minute needs to occupy about 10M bytes of storage capacity, that is, the capacity of a CD turntable is only about 1 hour. Of course, the problem is even more pronounced in the world of much higher bandwidth digital video. Are all these bits necessary? The study found that there is a large redundancy in the direct use of the PCM code stream for storage and transmission. In fact, sound can be compressed at least 4:1 under lossless conditions, that is, only 25% of the digital amount is used to retain all the information, and the compression ratio in the video field can even reach to several hundred times. Therefore, in order to use limited resources, compression technology has received much attention since its inception. The research and application of audio compression technology has a long history, like A-law coding, u-law is a simple almost instant compression technology, and has been applied in ISDN voice transmission. Research on speech signals has been developed before and has matured, and has been widely used, such as adaptive differential PCM (ADPCM), linear predictive coding (LPC), and other technologies.

Does MP3 affect the sound quality?

The compression of songs affects the quality, but the losses are not necessarily audible.

Is compression of MP3 songs harmful to the sound quality? Whether it is HD music or “normal” definition, the question of compression remains. The advantage is that the weight of the songs is reduced, so they take up less space in the memory of a phone or a portable music player. With standard MP3 compression, a music album ranges from 500 MB to 45 MB.

But by the way, the music is damaged. The sound seems a little less natural, less precise, less dynamic. Some of the audio information is literally destroyed. It doesn’t always sound good, but for some songs the difference is clear until everyone will notice.

Fortunately, you can improve the quality of an MP3 song by compressing it with less force. The loss of sound quality becomes less clear, but in return the song weighs more. MP3 isn’t the only compressed music format that corrupts music. The most famous competitors are AAC, Ogg Vorbis and WMA. MP3 is not the most efficient compression format, this title applies to the Ogg Vorbis, but it is still a good option. All music players can play MP3 and online record stores prefer this format.

Lossless compression

However, some music lovers are reluctant to MP3. They swear by “nondestructive” compression, which does not remove sound information. The music has been completely preserved: we hear absolutely no difference. The best known non-destructive formats are Flac, APE and Alac. Unfortunately, not all electronic devices can play music recorded in these formats. Few artists offer their music in “non-destructive” compression. And the weight of the parts thus compressed is still very heavy. An album quickly reaches several hundred megabytes. However, the Flac stands out as the reference format for the most demanding music lovers.

Is it reasonable to keep using MP3? This remains a smart choice for most music lovers, as long as they choose an appropriate compression ratio. Which one to choose: 192 kbit / s, 256 kbit / s or 320 kbit / s? The stronger the compression, the lighter the number, but the lower the quality. With 128 kbit / s, the sound has clearly deteriorated, most of us can hear it. At 192 kbit / s, degradation becomes difficult for most of us to observe except for some rare numbers.

With 256 kbit / s, you have to have a musical ear and good sound equipment to make the difference. With 320 kbit / s, you need a well-trained ear and highly accurate audio equipment to make a difference. We only see a difference in quality in certain titles and only in certain passages. Therefore, most of us can settle for 192 kbit / s recording. Music lovers should expect a minimum of 256 kbit / s. And professionals will choose formats of 320 kbit / s or ‘lossless’.

Data compression techniques

It is evident that coding techniques for multimedia information contain large amounts of data that require memory space for recording and high transmission speed for transfer to other digital systems.

These needs can be met by reducing the space occupied by the data with special compression techniques. Compressed data cannot be used directly for processing, viewing, or playback. Compression techniques are used by special programs immediately before data storage or transmission. During the read or receive phase, similar programs perform decompression. Compression can be done on the basis that information encoding techniques dedicate an always equal amount of memory to each information element (be it a character, a pixel or a sound sample), regardless of their statistical frequency and its significance.

The compression techniques developed so far are more than a hundred but grouped into two categories:

Compression without loss of information.

Lossless compression techniques are based on compact coding of the same data streams or coding with a small number of bits of the most statistically frequent data.

This compression is completely reversible and the decompression program returns the exact bit sequence as it originally was. For this reason, loss-free technique is applicable to any type of data, including executable texts and programs, although the achievable compression factor is not very high: values usually range from 2: 1 to 4: 1. Of course, these results vary depending on the type of input data.

RLE encoding

The RLE (Run Length Encoding) compression technique is oriented to equal byte sequences. In the original version, it provides the introduction of a special character that indicates the beginning of a sequence, and instead of encoding the same characters in the sequence one by one, it encodes only the first one, followed by a number indicating where many times drawn and repeated. Specifies with the Sc character at the beginning of the sequence, the statement

these ******** are eight stars… these Sc * 8 are eight stars

where 8 is not encoded as an ASCII character but as a binary number.

The decompression program interprets the next byte as a counter and rebuilds the original sequence.

For image compression, RLE encoding only works well with images that contain large areas of uniform color, but are not very effective with complex images.

Compression with loss of information.

Loss-free compression techniques are not sufficient to solve the problem of the huge amount of data generated by encoding multimedia information, e.g. Video images while allowing better use of memory space on disks or data transmission lines. High resolution. , audio or video.

However, to try to solve this problem, it is necessary to remember that multimedia information, although subject to transformation, can remain understandable; This allows for compression factors that are higher in some orders of magnitude than those observed.

These interventions can be studied based on the behavior (vision and hearing) of our sensory systems to reduce the required memory without obvious changes in information content. Compression techniques that do this are called “lossy” since the least significant piece of information is irreversibly suppressed. Therefore, it appears that the bitstream after decompression is different from the original, and therefore these techniques cannot be used for other types of information, e.g. Text. Furthermore, the information thus compressed is not suitable for further processing as the loss introduced with each subsequent step becomes more and more apparent.

What is video encoding and how does it work?

The technique of compressing videos

What do we mean when we talk about video coding or, as industry experts generally call it, video coding?

Simply put, video encoding is the process of compressing and converting video content. The ultimate goal is to use less storage space, use less bandwidth, and make the user experience smoother. It goes without saying that the compression process causes a significant loss of information. The more data that is applied, the more data is deleted in the video. The result is a different version of the original due to missing data.

Why is video coding so important?

Video encoding is essential for transmission because it simplifies the transmission of video on the Internet through a compression process. Compression reduces the bandwidth required while providing a high quality experience. Without this, raw video content would not allow many users to view content on the Internet due to insufficient connection speeds. The protagonist of this process is the bit rate or the speed of digital data transmission that can be transmitted in a certain time interval in a communication channel. When streaming, the bit rate determines whether users can easily view the content or are exposed to video buffering.

Another fundamental aspect of video coding is compatibility. Indeed, sometimes the content is already compressed to an appropriate size, but it still needs to be encoded to be compatible with different devices and applications, although this is often referred to as transcoding.

The video encoding process is governed by video codecs, which are compression standards that are created through software or hardware applications. Each codec consists of an encoder for compressing the video and a decoder for restoring an approximation of the video for playback. The name codec is actually derived from the merging of the words “encoder” and “decoder”.

But what is the best codec?

It depends on the type of video. On this occasion we will describe the most commonly used.

To stream high quality video over the Internet, H.264 is arguably the most widely used codec for most multimedia traffic. This codec is considered to be of excellent quality, coding speed and compression efficiency, although it is not as efficient as the later HEVC (High Efficiency Video Coding) compression standard, also known as H.265. H.264 also supports 4K video streaming, a real advance for a codec created in 2003.

Now that we have an overview of codecs, let’s look at some compression techniques.

Compression techniques

The most common compression technique is scaling the resolution. The higher the resolution of a video, the more information is contained in each picture. One way to reduce the amount of data is to reduce the size of the image and then scan it again. As a result, fewer pixels are generated, which reduces the level of detail of the image, which has a positive effect on the amount of information required. This process allows you to set multiple quality levels for a video that correspond to different resolutions created. A practical example is if you are watching a movie in streaming before playing it, you can actually choose the resolution at which you want to watch it, provided your device
Support him

One video compression technique that may not be widely used is the interframe. This process reduces “redundant” information from one frame to another.

Another technique is the P-frame, short for predictive frame, which means that it can look back at an i-frame or another P-frame and understand whether the same images are present. In this case, this part is excluded for reasons of space.

B-Frame, on the other hand, is the bidirectional predictive frame that offers good compression without affecting the viewing experience. However, this technique requires a higher coding profile.

Another technique is that which makes it possible to intervene in the color. This process, called “chroma subsampling”, tries to maintain the brightness of the image, which affects the quality of the color. Finally, another method of compressing videos is to reduce the number of frames per second.