Data Compression: Unlocking Efficiency in Computer Vision with Data Compression

By Fouad Sabry

What is Data Compression

In information theory, data compression, source coding, or bit-rate reduction is the process of encoding information using fewer bits than the original representation. Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by removing unnecessary or less important information. Typically, a device that performs data compression is referred to as an encoder, and one that performs the reversal of the process (decompression) as a decoder.

How you will benefit

(I) Insights, and validations about the following topics:

Chapter 1: Data compression

Chapter 2: Audio file format

Chapter 3: Codec

Chapter 4: JPEG

Chapter 5: Lossy compression

Chapter 6: Lossless compression

Chapter 7: Image compression

Chapter 8: Transform coding

Chapter 9: Video codec

Chapter 10: Discrete cosine transform

(II) Answering the public top questions about data compression.

(III) Real world examples for the usage of data compression in many fields.

Who this book is for

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Data Compression.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: May 5, 2024

Book preview

Chapter 1: Data compression

In information theory, data compression, source coding, and other related fields: In common parlance, a device that engages in the process of data compression is known as an encoder, whereas a device that engages in the process's inverse—that is, decompression—is known as a decoder.

Data compression is the process of lowering the size of a data file, and is a term that is used rather often. Source coding is an encoding process that takes place at the original data source, prior to the data being stored or transferred. This process is referred to in the context of data transmission. It is important not to mistake source coding with other types of coding, such as channel coding, which is used for error detection and correction, or line coding, which is a method for mapping data onto a signal.

Data compression is beneficial since it cuts down on the amount of space and bandwidth needed to store and transfer information. The procedures of compression and decompression both need a significant amount of computational resources. The space-time complexity trade-off is something that must be considered while compressing data. For example, a video compression method might call for expensive hardware in order for the video to be decompressed quickly enough to be watched as it is being decompressed. Additionally, the option to fully decompress the video before watching it might be inconvenient or call for additional storage space. When designing data compression schemes, designers must make trade-offs between a number of different factors. These factors include the level of compression achieved, the amount of distortion that is introduced (when using lossy data compression), and the amount of computational resources that are needed to compress and decompress the data.

In order to represent data without losing any information in the process, lossless data compression methods often make use of statistical redundancy. This ensures that the process may be reversed. Because the vast majority of data in the actual world has statistical redundancy, lossless compression is feasible. For instance, a picture may include patches of color that do not change over the course of multiple pixels; in this case, the data may be recorded as 279 red pixels rather of the traditional notation of red pixel, red pixel,... This is a fundamental illustration of run-length encoding; there are many more methods to decrease the size of a file by removing redundant information.

Compression techniques such as Lempel–Ziv (LZ) are now among the most widely used algorithms for lossless data storage. Table entries are replaced for repeating strings of data in the LZ technique of compression, which is a table-based compression model. This table is built dynamically for the vast majority of LZ algorithms by using data from previous stages of the input. Most of the time, the table itself is Huffman encoded. Grammar-based codes like this one are capable of successfully compressing substantially repetitious input, such as a biological data collection of the same or nearly related species, a massive versioned document collection, internet archives, and so on. Constructing a context-free grammar that derives a single string is the fundamental undertaking of grammar-based coding systems. Sequitur and Re-Pair are two further techniques for compressing grammar that have practical applications.

Probabilistic models, such as prediction by partial matching, are used in the most powerful lossless compressors developed in recent times. Indirect statistical modeling is another way to think about the Burrows–Wheeler transform, which you may also consider.

Around the same time as digital photos were becoming more widespread in the late 1980s, the first standards for lossless image compression were developed. At the beginning of the 1990s, lossy compression techniques started to become more commonplace. These perceptual distinctions are used by a variety of well-known compression formats, such as psychoacoustics and psychovisuals, respectively, for the compression of sound and pictures and video.

Transform coding is the foundation for the vast majority of lossy compression methods, particularly the discrete cosine transform (DCT). It was first conceived of by Nasir Ahmed in 1972, and he went on to construct a functioning algorithm with the assistance of T. Natarajan and K. R. Rao in 1973. Nasir Ahmed presented the idea for the first time in January 1974. audio and video (in formats such as MPEG, AVC, and HEVC) (such as MP3, AAC and Vorbis).

In order to enhance storage capabilities, digital cameras use a kind of picture compression known as lossy. DVDs, Blu-rays, and streaming video are all examples of video formats that employ lossy video coding. Lossy compression is widely employed in the video industry.

In the process of lossy audio compression, techniques from the field of psychoacoustics are used to strip the audio signal of components that are inaudible or audible to a lesser degree. Speech coding is considered to be a different field from general-purpose audio compression since the compression of human speech often requires the use of even more specialized methods. For example, speech coding is utilized in internet telephony. Audio compression is used for CD ripping, and audio players are responsible for decoding the compressed files.

Lossy compression may cause generation loss.

The information theory and, more specifically, Shannon's source coding theorem serve as the theoretical foundation for compression; domain-specific theories include algorithmic information theory for lossless compression and rate–distortion theory for lossy compression. Claude Shannon is mostly credited with initiating these subfields of research when he published a number of seminal articles on the subject throughout the latter half of the 1940s and the early 1950s. Coding theory and statistical inference are two related but distinct subjects that also pertain to compression.

The concepts of machine learning and compression are intricately related to one another. In order to achieve the highest possible level of data compression, a system that is capable of predicting the posterior probability of a sequence in light of its complete history is ideal (by using arithmetic coding on the output distribution). On the other hand, a perfect compressor may be used for predictive purposes (by finding the symbol that compresses best, given the previous history). This comparability has been used as argument for the utilization of data compression as a standard for universal intelligence..

According to AIXI theory, which is a relationship that is more clearly stated in Hutter Prize, the smallest possible software that creates x is the greatest possible compression of x that is conceivable. For instance, according to that model, the compressed size of a zip file takes into account both the zip file and the software necessary to unzip it, given that you cannot unzip it without both, although there may be a combined form that is even more compact.

Software like as VP9, NVIDIA Maxine, AIVC, and AccMPEG are all examples of audio and video compression programs that are driven by AI.

The process of data compression may be thought of as a subset of the data differencing process. Data patching is the process of recreating the target given a source and a difference, while data differencing is the process of making a difference between a source and a target given just the difference. Since there is no such thing as a distinct source and destination in data compression, one may think of it as data differencing with empty source data. This means that the compressed file is equivalent to a difference from nothing. This is the same as considering relative entropy, which corresponds to data differencing, to be a particular case of absolute entropy, which corresponds to data compression, but with no beginning data.

The data differencing relationship is emphasized by the usage of the phrase differential compression.

The Shannon–Fano coding algorithm was the forerunner of entropy coding, which was developed in the 1940s, The compression of audio data, not to be confused with the compression of dynamic range, has the ability to lessen the bandwidth required for the transmission of audio data as well as the storage needs for audio data. Audio compression methods are often referred to as audio codecs when they are implemented in software. It is possible to reduce the amount of redundant information in both lossy and lossless compression by employing techniques such as coding, quantization, DCT, and linear prediction. These techniques are aimed at lessening the amount of information required to accurately represent the original, uncompressed data.

Many different audio programs, such as MP3 and Vorbis, make use of lossy audio compression methods since they provide greater compression and give better quality. These algorithms