Data compression

Computers can make files small.

This helps them save space. It also helps them send things fast.

There are two ways to do this. One way keeps all the details. This is called lossless. No information is lost.

The other way makes files even smaller. It does this by removing bits. These bits are not very important. This is called lossy.

Lossless works by finding patterns. For example, it sees many red dots. It says "279 red dots" instead of listing each one.

Lossy is used for many things. It is used for videos and songs. It even helps digital cameras. It makes sure you have room for more photos.

Computers use data compression to make files smaller. This helps them save space. It also helps them send files quickly.

There are two main ways to do this.

Lossless compression keeps all the original information. It works by finding patterns. For example, an image might have many red pixels in a row. Instead of listing every single pixel, the computer writes "279 red pixels." This is called run-length encoding. One popular way to do this is called LZW. This method uses a table to swap repeated parts of data.

Lossy compression makes files even smaller. It does this by removing bits that are not important. This method accepts some loss of detail to save space. It is often based on how people see or hear. For example, the human eye is very good at seeing brightness. But it is not as good at seeing small changes in color.

Lossy coding is used for many things. It is used for JPEG images and MP3 music. It is also used for streaming videos on the internet.

Data compression is the way we make digital files smaller. This is very important for saving space on devices. It also helps us send information across the internet much faster.

There are two main ways this happens. One way is called lossless compression. This method keeps every single piece of original information. The other way is called lossy compression. This method makes files even smaller by removing bits of data that are not essential.

Lossless compression works by finding patterns in the data. This is often called statistical redundancy. For example, an image might have a long row of the same color. Instead of listing every single pixel, the computer can just say "279 red pixels." This specific way is called run-length encoding. Other methods, like Lempel-Ziv (LZ) algorithms, use a table to swap repeated parts of data. This allows the computer to rebuild the exact original file later.

This process is reversible, so nothing is ever lost.

Lossy compression works differently by making choices about what to keep. It is designed based on how humans see and hear. For instance, our eyes are very sensitive to brightness. However, we are not as good at seeing small changes in color. Lossy methods can remove those less important color details to save space. This is how JPEG images work. In music, methods called psychoacoustics remove sounds that people cannot easily hear. This allows for smaller files like MP3s.

Many important scientists helped develop these ideas. Claude Shannon wrote fundamental papers on this topic in the late 1940s and early 1950s. He created the field of information theory. In 1972, Nasir Ahmed proposed a method called the discrete cosine transform (DCT). He later worked with T. Natarajan and K. R. Rao to create a working version in 1973. This DCT method is now used for many things. It is found in JPEG images, MPEG videos, and MP3 audio files.

Today, we see compression in almost everything we do online. When you stream a video, you are using lossy video coding. When you save a photo on a digital camera, compression helps you fit more pictures. Even modern machine learning is connected to this. For example, a model called Chinchilla 70B from DeepMind showed it could compress data very well. It compressed images to 43.4% of their original size. This shows how much technology continues to improve at handling our digital world.

Data compression is the process of encoding information using fewer bits than its original representation. In the digital world, this is often called source coding or bit-rate reduction. This process is vital because it makes data files smaller. Smaller files allow for more efficient storage on devices. They also enable much faster data transmission across networks.

Compression is categorized into two primary types: lossless and lossy. The choice between them depends on whether the user requires the exact original data or can accept a slightly altered version to save more space.

Lossless compression works by identifying and eliminating statistical redundancy. This means the algorithm finds patterns or repetitions within the data. Because it only removes redundant information, the process is entirely reversible. No information is lost during encoding, so a decoder can reconstruct the original file perfectly.

A simple example is run-length encoding. Instead of recording "red pixel, red pixel, red pixel," the system records "3 red pixels." This method is highly effective for data that contains many repeating sequences.

Several advanced algorithms drive lossless compression. The Lempel–Ziv (LZ) methods are among the most popular for storage. One variation, called DEFLATE, is optimized for both compression ratio and decompression speed. In the mid-1980s, the Lempel–Ziv–Welch (LZW) algorithm became a standard for many systems. LZW uses a table-based model where repeated strings of data are replaced by table entries. This method is used in GIF images and hardware like modems. Other methods include grammar-based codes like Sequitur and Re-Pair, which are effective for highly repetitive data.

Lossy compression takes a different approach by removing unnecessary or less important information. This method accepts a certain amount of data loss to achieve much higher compression ratios. Designers base these schemes on how humans perceive sensory input. For example, the human eye is more sensitive to changes in luminance, or brightness, than to changes in color. Lossy image compression, such as the JPEG standard, works by rounding off these nonessential bits. Similarly, lossy audio compression uses psychoacoustics to remove sounds that are difficult for humans to hear.

Most lossy compression relies on transform coding, specifically the discrete cosine transform (DCT). This mathematical process was first proposed by Nasir Ahmed in 1972. He later developed a working algorithm with T. Natarajan and K. R. Rao in 1973. The DCT is now a cornerstone of modern multimedia. It is used in JPEG and HEIF image formats, as well as MPEG, AVC, and HEVC video formats. It is also essential for audio formats like MP3, AAC, and Vorbis.

The theoretical foundations of this field were established by Claude Shannon. In the late 1940s and early 1950s, Shannon published fundamental papers that created information theory. His work provided the basis for understanding how information can be encoded. Other related mathematical areas include algorithmic information theory for lossless methods and rate–distortion theory for lossy methods. These theories help engineers understand the mathematical limits of how much data can be compressed.

Modern technology has even linked compression to machine learning. An optimal compressor can actually be used for prediction by finding the symbol that compresses best. This connection has led some to use compression as a benchmark for general intelligence. In unsupervised machine learning, the k-means clustering algorithm can compress data by grouping similar points into clusters. This reduces large datasets into a smaller set of representative points. Even large language models (LLMs) show compression abilities. DeepMind's Chinchilla 70B model successfully compressed images to 43.4% and audio to 16.4% of their original sizes.