Data compression refers to reducing the amount of data to reduce storage space, improving storage, storage, and processing efficiency without losing useful information, or reorganizing data according to certain algorithms, reducing data redundancy and storage space. a technical method. Data compression includes lossy compression and lossless compression.
In computer science and information theory, data compression or source coding is the process of representing information in terms of a specific coding mechanism with fewer data bits (or units associated with other information) than unencoded. For example, if we encode "compression" as "comp" then this article can be represented with fewer data bits. A popular compression example is the ZIP file format used by many computers. It not only provides compression, but also acts as an archiver (Archiver), which can store many files in the same file.
For any form of communication, compressed data communication works only when both the sender and the receiver of the message understand the encoding mechanism. For example, this article only makes sense if the recipient knows that the article needs to be interpreted in English. Similarly, he can understand compressed data only when the recipient knows the encoding method. Some compression algorithms take advantage of this feature to encrypt data during compression, such as password encryption, to ensure that only the authorized party can get the data correctly.
Data compression can be achieved because most real-world data has statistical redundancy. For example, the letter "e" is more common in English than the letter "z", and the probability that the letter "q" is followed by "z" is very small. Lossless compression algorithms typically utilize statistical redundancy so that the sender's data can be represented more concisely, but still completely.
Further compression can also be achieved if a certain degree of fidelity loss is allowed. For example, people may not notice that some details are not perfect when they look at pictures or TV pictures. Again, the two audio recording sample sequences may sound the same, but they are not exactly the same. Lossy compression algorithms use less bits to represent images, video, or audio with minor differences.
Compression is very important because it can help reduce the consumption of expensive resources such as hard disk space and connection bandwidth, but compression consumes information processing resources, which can also be expensive. So the design of the data compression mechanism requires a compromise between compression capabilities, distortion, required computing resources, and other different factors that need to be considered.
Some mechanisms are reversible so that the original data can be recovered. This mechanism is called lossless data compression. Other mechanisms allow a certain amount of data loss in order to achieve higher compression ratio. This mechanism is called lossy data compression.
However, there are often files that cannot be compressed by the lossless data compression algorithm. In fact, any compression algorithm that does not contain identifiable data cannot be compressed. Attempts to compress data that has already been compressed usually result in actually expanding the data, and attempts to compress the encrypted data usually yield this result.
In fact, lossy data compression will eventually reach a point where it can't work. Let's take an extreme example. The compression algorithm removes the last byte of the file each time, and then the algorithm compresses continuously until the file becomes empty, and the compression algorithm will not continue to work.
The necessity of multimedia data compressionAn important feature of the information age is the digitization of information. The digital video and the media information such as frequency have a large amount of media, which is far from the computer storage resources and network bandwidth that current hardware technology can provide. In this way, it is very difficult to store and transmit multimedia information, which becomes a bottleneck problem that hinders people from effectively acquiring and utilizing information. Without the effective compression of multimedia data, it is difficult to ensure the smooth progress of communication. The data of the video and audio signals of Ninghua Ding is very amazing. The following is an example to illustrate.
For audio information, the audio of a person in normal speech is generally 200 Hz to 3.4 kHz, that is, the width of each human language is about 3.4 kH:. Similarly, according to the sampling theorem, if the digitization precision is 8btt, the data amount per second is 3.4×2×8=54.4kb, that is, the data amount of 1 minute under the above sampling condition is about 400 kb.
Taking a general color television signal as an example, the bandwidth of each component in the YIQ space representing light intensity, color, and color saturation is 4 MHz, I. 3MH s and o. 5MHz. According to the sampling theorem, only when the sampling frequency is greater than or equal to 2 times the frequency of the original signal.åˆ 'Ensure that the sampled signal can be restored to the original signal without distortion. Then set each sample to be digitized to 8bit. Thus, the data amount of the TV signal of 1 second is (4:1.3.30 o.5)×2×8=92.8Mb. The original TV data of the 640 MB capacity cD-R()M can be stored. With 2 check digits) for Li black and black - dd s
That is to say, "Zhang ordinary light can only store the original data of 44s. It supports the end claw speed and signal holding parts necessary for high-quality storage and transmission of multimedia signals such as voice, image and video.
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