Data compression is a commonly used technology that is present everywhere in the modern world. For example, web pictures are compressed in JPEG or GIF formats and HDTV is usually compressed using the MPEG-2 format. Compressing data saves space and bandwidth, but it requires complex algorithmic tools and formulas to work.

Compression Basics

The most commonly compressed files are audio and video files, but other digital data like Outlook emails and operating system files are regularly compressed. Compressing data is an important way to save hard drive space, speed up downloading time and reduce wasted space. The actual task of compression consists of two separate components. First, an encoding algorithm that takes the digital item and creates a compressed copy. Second, algorithm decoding occurs and the original item is recreated. These mutually interconnected components must share standards because they must mutually communicate with each other in order to recreate the original item.

Lossless vs. Lossy

There are lossless algorithms, which recreate the original item precisely from the compressed data. There are lossy algorithms that only recreate an estimate of the original digital item. Lossless algorithms are usually used for texts and they guarantee high quality. Lossy for used for images and sound when a slight loss in resolution is undetectable or acceptable. Most people assume that the word lossy connotes poor quality compression that results in missing images or characters. However, lossy is term that doesn’t imply randomly lost data, but means the slight loss of overall quantity, such as sound frequency or video pixels.


Data must be decompressed in order to be accessed and used. Decompression is the opposite process of compression. However, the coders and decoders are implemented very differently. That is, symmetric coding is characterized by good quality and is especially desirable for dialogue applications. Asymmetric coding techniques are less costly than the original coding process. Asymmetric coding is intended for applications with one-time compressions and decompressions that frequently occur, or for applications that need the decompression process to quickly order. This is most common used with pictures and video to increase the quality of the compressed images. For example, JPEG, which standards for Joint Photographic Experts Group, is used for still images and H.263 is used for low-resolution video sequences for mobile communications.

The Steps for Compression

There are standard sequences of operations for the compression of still image, video and audio data. To compress a picture, the first step is to generate an appropriate digital representation of the target item. For instance, a picture might be divided into eight equally sized blocks that each contain a fixed number of bits per pixel. Second, the processing step uses various compression algorithms. When it comes to interframe coding for videos, motion vectors will be established for each of the eight blocks. Third, quantization, or digital mapping, occurs after the mathematically identical picture is created. Each of the blocks is based on a specific resolution and characteristic curve. Entropy coding starts with a stream of sequential bits and bytes of data. After the four compression steps, the digital data are placed in a data stream with an established format and an error correction code may be added.

Data compression is a common way to store and access various digital items like JPEG pictures, MP3 song files and MKV video files.

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