What are the important indicators to measure the performance of data compression technology?

There are three important indicators to measure the performance of data compression technology: 1. Data compression ratio refers to the proportion of data being compressed; 2. The algorithm to achieve compression must be simple, which means the data compression speed is fast; 3. The data recovery effect should be good and the original data should be restored as completely as possible.

The operating environment of this tutorial: Windows 7 system, Dell G3 computer.

Data compression technology is a technology that uses the least number of digits to represent signals.

Due to the huge data volume of digital multimedia information, especially digital video and audio signals, it will be difficult to achieve practical application without effective compression. Therefore, data compression technology has become a key common technology in today's digital communications, broadcasting, storage and multimedia entertainment.

Indicators for measuring data compression methods: compression ratio, speed, and effect.

• Data compression ratio

  Data compression ratio (English name: data compression ratio) is a measure of the quality of the compression efficiency of a data compressor index. It refers to the proportion of data being compressed.

• The compression algorithm should be simple, that is, the data compression speed should be fast;

• The data recovery effect should be good , to completely restore the original data as much as possible
  

Data compression refers to reducing the amount of data to reduce storage space and improve its transmission without losing information. , a technical approach to storage and processing efficiency. Or reorganize the data according to a certain algorithm to reduce data redundancy and storage space.

Data compression is divided into two categories, with three classification methods:

1. Instant compression and non-instant compression

Instant compression is to compress the voice signal It is converted into a digital signal, compressed at the same time, and then transmitted instantly through the Internet. Real-time compression is generally used in the transmission of video and audio data.

Non-real-time compression is performed when needed and has no real-time nature. Non-instant compression generally does not require special equipment, just install and use the corresponding compression software directly on the computer.

2. Digital compression and file compression

Digital compression refers specifically to some time-sensitive data, which are often collected, processed or transmitted in real time.

File compression refers specifically to the compression of data to be stored in physical media such as disks, such as the compression of an article data, a piece of music data, a piece of program encoding data, etc.

3. Lossless compression and lossy compression

Lossless compression uses the statistical redundancy of data to compress, so the compression ratio of lossless compression is generally lower. This type of method is widely used in the compression of text data, programs and image data in special applications that require accurate storage of data.

The lossy compression method takes advantage of the fact that human vision and hearing are insensitive to certain frequency components in images and sounds, allowing a certain amount of information to be lost during the compression process. Lossy compression is widely used to compress voice, image and video data.

Extended information: Application of data compression

A very simple compression method is run length encoding, which uses simple encoding such as data and data length instead Same for continuous data, this is an example of lossless data compression. This method is often used on office computers to better utilize disk space, or to better utilize bandwidth in a computer network. Losslessness is a very critical requirement for symbolic data such as spreadsheets, text, executable files, etc., because except for some limited cases, even a change of one data bit is unacceptable in most cases.

For video and audio data, a certain degree of quality degradation is acceptable as long as important parts of the data are not lost. By exploiting the limitations of the human perceptual system, storage space can be saved significantly and the quality of the results obtained is not significantly different from the quality of the original data. These lossy data compression methods often require a compromise between compression speed, compressed data size, and quality loss.

Lossy image compression is used in digital cameras to greatly increase storage capacity with almost no loss in image quality. The lossy MPEG-2 codec video compression for DVD implements similar functionality.

In lossy audio compression, psychoacoustic methods are used to remove inaudible or difficult-to-hear components of the signal. The compression of human speech often uses more professional techniques, so people sometimes distinguish "speech compression" or "speech coding" as an independent research field from "audio compression". Different audio and voice compression standards belong to the category of audio codecs. For example voice compression is used for Internet telephony, while audio compression is used for CD ripping and decoding using MP3 players.

The theoretical basis of theoretical compression is information theory (which is closely related to algorithmic information theory) and rate-distortion theory. The research work in this field was mainly laid by Claude Shannon, who worked in the late 1940s and early 1950s. Published fundamental papers in this area. Doyle and Carlson wrote in 2000 that data compression "has one of the simplest and most elegant design principles in all engineering fields". Cryptography and coding theory are also closely related subjects, and the idea of ​​data compression has deep roots in statistical inference.

Many lossless data compression systems can be viewed as four-step models. Lossy data compression systems usually contain more steps, such as prediction, frequency transformation, and quantization.

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