Data Compression: Definition, Types and Algorithms

WinRAR, 7z, Zip Files, MP3 and 3GP, we have all heard about them. What exactly are these file formats? They’re compressed file formats that use a process called Data Compression; which restructures or modifies Data to reduce its size. This process usually involves re-encoding information by reducing the bit-size of a file. 

In this blog, we will explain what Data Compression is, the types of Data Compression, the methods of Data Compression and the best practices. At the end of this you will be able to gauge the importance of Data Compression, and you can apply certain practical that you learned from this to your or your organisation’s processes. 

Table of Contents 

1) What is Data Compression? 

2) How Data Compression Functions? 

3) The Importance of Data Compression 

4) Types of Data Compression 

5) Applications of Data Compression 

6) Techniques of Data Compression 

7) Data Compression Algorithms 

8) Best Practices for Data Compression 

9) Benefits of Data Compression 

10) Drawbacks of Data Compression 

11) Conclusion 

What is Data Compression? 

Data Compression can be defined as the process of shortening the sizes of files using specialised algorithms to save storage process or for sending it across computers through internet or internally via LAN. Data Compression can be achieved with market ready tools like WinZip/WinRar/7-Zip or through custom algorithms.
 

 

How Data Compression Functions? 

Depending on the form of data you want to compress, the process may vary a bit. But the way it functions is simple: Identify the patterns and redundancies and repackage them using short codes or symbols.  This may be true for text and image files but how it works for audio and videos is different as stated earlier: 

1) Audio Files are shrunk through using lossy techniques which involve eliminating white noise and background noise.  

2) For Video Files due to the large volume of images and audio files; there are specific methods or software that can handle the complex removal of background noise, white noise and redundancies of the video. 

The Importance of Data Compression 

Data Compression allows you to reduce file size opening crucial space on the storage front. Not only does it save disk space, the bandwidth required to transfer compressed files is lower enabling efficient data transfer and reduced costs. While lossy compression provides a balance of quality and file size, lossless compression provides data integrity over file size. 

Types of Data Compression 

Depending on what type of data you want to compress, there are various methods of doing it. We will be looking at Types of Data Compression in this section. 

1) Text Compression 

Text compression is probably the easiest to understand. A compression algorithm for text compression primarily uses codes or symbols for repetitive patterns and redundancies; enabling a reduction of size but with unaltered data integrity. 

2) Audio Compression 

In Audio Compression, the audio you provide is scanned by the encoding algorithm and eliminates background noise and white noise to shrink the file. The drawback of audio compression is that the audio loses quality upon rendering it. 

3) Image Compression 

Image is compressed quite like text and instead of replacing text, the encoder replaces repetitive pattern of colours and uses codes to reduce the size. Again, the drawback is the loss of quality and resolution in the image, but it maintains the data integrity. 

4) Video Compression 

Using a combination of audio and image compression, encoders specialising in video compression remove unwanted images and background noise to reduce the overall size. This type of compression also causes loss of quality. 

Perform complex data analysis with our Data Analysis Training Using MS Excel Register now! 

Applications of Data Compression 

From sending file across computers to audio streaming for slower internets, Data Compression has various applications. We will be looking at a few in this following section: 

1) Audio Compression 

The world of audio compression and its utilities are endless. Here are some practical applications that is around you in your day-to-day life: 

1) Streaming Services: Platforms like Spotify and Apple Music use compression encoded as MP3 or AAC to reduce bandwidth while maintaining audio quality. 

2) Telecommunications: VoIP calls and mobile networks constantly use codecs like Opus, G.711 to ensure clear communication with minimal data usage. 

3) Broadcast: Radio and TV Stations compress audio for efficient transmission. 

4) Hearing Aids: Compression helps in amplifying essential sounds while reducing background noise. 

5) Gaming and Virtual Reality: Reduces audio latency and improves real-time audio processing.
 

2) Video Compression 

Video compression must take place for efficiently storing, transmitting, and streaming video content. 

1) Streaming Services: Netflix, YouTube, Prime, Hulu are some of the prominent streaming giants that use H.264/H.265 codec to deliver high quality video content while using a negligible bandwidth, 

2) Video Conferencing: The world of Zoom Meetings and Microsoft Teams use compression for smooth, real-time communication. 

3) Surveillance Systems: Closed Circuit security cameras often use video compression to store footage more efficiently. 

3) Communication 

Data Compression in Communication is majorly used for: 

1) Email: For reducing attachment size 

2) Wireless Communication: For maximising the use of bandwidth 

3) Satellite Communication: For reducing transmission times 

4) Cloud Computing 

When it comes to cloud computing compression of files is essential. It enables storage optimisation, data transfer with efficient bandwidth allotment, backup and archiving old data with low space usage, faster transfer CDNs for end users and encryption through compression. 

5) Healthcare 

In an hospital, hundreds of patients come in and go. The amount of data points generated per patient is huge ranging from Anthropometric data to Diagnostics data. In such cases where systems are stretched thin, it would be of great help to health care institutions if they use Data Compression to store data. This will also help in encrypting certain confidential patient details. 

Techniques of Data Compression 

Data Compression involves using various techniques to reduce the size of data files while maintaining their usefulness. 

Run-Length Encoding 

RLE or Run Length Encoding is a form of lossless Data Compression. In this form of compression runs of data, which are consecutive occurrences of the same data are stored in a single occurrence of the data along with number of runs. This type of Data Compression was first used to compress B&W images supported by Compuserve and later was adapted for Graphic Interchange Format (GIF). 

Dictionary-Based Coding 

A dictionary in the context of Data Compression is a collection of phrases or strings that frequently occur in data. Once a dictionary is created, the compression begins by scanning the data for frequent occurrences of the phrase for a match. As soon as a match is found, all the occurrences are replaced with a shorter code or symbol. 

Perceptual Coding 

Perceptual Coding is an audio compression algorithm that is lossy in nature and mostly used by radio stations. It has a flexible compression format with 16Kb/s for monophonic channel devices to 1024 Kb/s for 5.1 format with four or six aux units. It also provides a near CD-quality audio for devices with stereo channels. 

Join the Apache Kafka Training Course and advance your Data Analysis Skills! 

Data Compression Algorithms 

Data Compression algorithms reduce the size of data files, making storage and transmission more efficient. These algorithms work by identifying and eliminating redundant information. 

Huffman Coding 

A lossless algorithm that is primarily used for data sets that consist of frequently occurring characters. Each character is encoded with a unique code based on its frequency. As an entire string is represented using this code, the size of the file is reduced but the data integrity is intact. 

Audio and Video Codecs 

There is huge variety of codecs available for the world of audio and video. For videos the following codecs are available: 

a) H.264 Lossless (Lossless) 

b)  Apple Animation QuickTime RLE (Lossless) 

c) Autodesk Animator Codec (Lossless) 

d) HEVC (Lossy) 

e) MPEG4 (Lossy) 

f) AVC (Lossy) 

g) H.264 and H.265 (Lossy Versions) 

While audio has following formats: 

a) MP3 (Lossy) 

b)  AAC (Lossy) 

c) Ogg Vorbis (Lossy) 

d) FLAC (Lossless) 

e) ALAC (Lossless) 

LZSS (Lempel-Ziv-Storer-Szymanski) Algorithm 

LZSS is an update over lossless format of compression. By finding repeated patterns of data points, it assigns them to a shorter code leading to a reduction in the file size. For example, instead of returning data like ‘hello, hello, hello, hello’, using the LZSS Algorithm you can return the data value as ‘hello x4’. 

DEFLATE 

This method is based on both LZSS and Huffman Coding algorithms. DEFLATE is lossless and was initially developed for ZIP files but nowadays it’s used for gzip in HTTP compression and PNG formats. It applies the Huffman Coding twice to make the size of the file incredibly low. DEFLATE is popular amongst UX engineers as it enhances browsing experience by compressing HTTP responses and reducing load times. 

Lempel-Ziv Algorithm 

The Lempel-Ziv Algorithm is a precursor to the LZSS Algorithm. Unlike the LZSS system, which changes frequent occurrences into shorter instances and removes the earlier occurrences, Lempel-Ziv Algorithm keeps the original source code. This makes the final file size a bit larger than usual. 

Learn data visualisation and Elastic Search with our ELK Stack Course - Register now! 

Best Practices for Data Compression
 

Benefits of Data Compression 

Here the following are the benefits of Data Compression:
 

Storage	With Data Compression, the storage required for files is significantly diluted and you will be able to store more information in short spaces.
Speeds	Data Compression aides in faster data transfer across networks; benefiting people operating businesses om the cloud or across the world.
Performance	Compressed data can be easily accessed and processed much faster than uncompressed data.
Versatility	A range of files ranging for image, text, audio and video files can be compressed.
Scalability	With Data Compression, storage becomes adaptable facilitating scalable avenues for the business.

 

Drawbacks of Data Compression 

Here the following are the drawbacks of Data Compression:
 

Power Demands	Compression of data requires huge amount of computational power often making the processes extremely expensive for businesses
Compression Threshold	Files cannot be compressed indefinitely. All these files have threshold beyond which compression is not possible.
File Size	There might be certain tools that may limit the size of the file, requiring multi-stage compression, which might to loss of origin of the data.
Quality	Since compression can also cause loss in data, there’s a constant worry about how the end quality of the product will be.
Security Concerns	Anti-viruses are often Achillies heeled by compressed files as they cannot scan every single file, opening systems to Cyber Vulnerabilities.

 

Conclusion 

Data Compression is a powerful process that allows you to reduce file sizes, free up storage space, and speed up file processing. The best part is that it works with various file formats and methods. However, while compression offers many benefits, it also comes with challenges, such as security risks, high computational demands, file size limits, and potential quality loss.