Most Popular Data Compression Algorithms 2022

admin October 28, 2022 - 10:44 am

The elimination of unnecessary data or reformatting the data gives great efficient results. The storage market has kept looking to offer solutions that involve data compression or storing data in such a format that requires less memory space than usual.

Data compression is the process of reducing file sizes while maintaining the same or a close approximation of data. What are the two common types of compression algorithms? There are two types and when you compress data, it can either be lossy or lossless.

  • Lossy methods permanently erase data.
  • Lossless methods preserve all original data.

We have discussed the 10 most popular types of compression algorithms below that you can use to compress your data easily.

10 Most Popular Data Compression Algorithms

There are various algorithms that you can use if you want to compress your file but on the other hand, you want your file to restore to its original format after recovering. These lossless data compression algorithms are actually used for archiving files.

1. LZ77 (Lempel-Ziv-77)

Which algorithm is best for data compression? Lempel-Ziv algorithms are considered to be the best data compression algorithms. LZ77 was announced in 1977. The lz77 compression algorithm uses a sliding window method. It manages a dictionary that uses triplets to representing

  • Offset: It is the actual start of the phrase and the start of the file.
  •  Run-length: It is the number of characters that help you in making of phrase.
  • Deviating characters: These are the marketers that display a new phrase.

It has a declaration that the phrase used in it is similar to the original phrase and also tells if there is any different or additional character.

As you parse a file, the dictionary is updated for the reflection of the compressed data contents and their sizes.

2. LZR 

It is a modified LZ77, released in 1981. It is a linear alternative to the LZ77 lossless compression algorithm. It needs a significant amount of data storage if you are not using it linearly.

  • It can be used for any offset within a file.
  • When implemented on hardware, it has a very high potential for performance.

It is the first data compression algorithm widely used on Computers as well as in Gif image formats.

3. LZSS (Lempel-Ziv-Storer-Szymanski)

It was introduced in 1982. It is the improvement of the LZ77 data compression algorithm.

  • It keeps an eye on whether the distribution has substitution the file size
  • If it has not decreased, the data will be in its original form
  • It is used for archiving files in different formats i.e. RAR, and ZIP for the compression of data.
  • It only uses offset length pairs and does not prefer the use of deviating characters.

4. Deflate

It was released in 1993. It combines LZ77 and LZSS preprocessor with Huffman coding. It is an entropy coding method that assigns codes based on the frequency of the characters. It has no patent available now.

5. LZMA (Lempel-Ziv-Markov chain Algorithm)

It was designed and released in 1998. It is a modification od LZ77. it was modified for 7zip archiver with a .7z format

  • It uses a dictionary compression scheme similar to the LZ77 data compression algorithm.
  • It features a high compression ratio and variable compression dictionary size, while still maintaining the speed of decompression.

6. LZMA2

It was designed and released in 2009. It is the modification of LZMA. it is the improved handling of incompressible data. LZMA2 has both uncompressed data and LZMA data with multiple LZMA encoding parameters.

  • It supports scalable multithreaded compression and decompression and compression of data that is partially incompressible.
  • It can be unsafe and less efficient than LZMA.
  • It is useful but not much safe as compared to LZMA.

4 Most Popular Image & Video Compression Algorithms

There are several data compression algorithms that you can use for detailed analysis. These are:

7. Multi-Layer Perceptron (MLP)-Based Compression

It is a technology that uses multiple neuron layers i.e. input, processing, and giving output data. It is implemented for the reduction of dimension tasks and the compression of data. It was released in 1988 and then entered the existing process.

  • Binary coding: it is the standard two-symbol coding.
  • Quantization: it is the problem of input from a continuous set to a discrete set.
  • Spatial domain transformation: It is the pixel-by-pixel changes to data.
  • It permits the accurate approximation of data completely based on adjacent data via backpropagation.

8. DeepCoder – Deep Neural Network-Based Compression

It performs the encoding of quality maps into the binary stream with the use of scalar quantization traditional file compression algorithm called Huffman encoding. It is able to provide superior performance in comparison to the H.264/AVC video coding standard.

9. Convolutional Neural Network (CNN) – Based compression

CNN’s give better compression results than MLP-based algorithms. It has improved super-resolution performance and artifact reduction. CNN’s are layered neural networks that are used for image recognition and feature detection

  • It helps achieve the performance of the High-Efficiency Video Coding (HEVC) standard by using entropy estimation. 

10. Generative Adversarial Network (GAN) – Based Compression

GANs are a form of neural network. GAN uses two networks to produce more accurate analyses and predictions. It was first developed in 2017. 

  • The GAN-based compression produces very high-quality images/videos for you by eliminating malicious loss.
  • It can compress files more than two and a half times smaller in comparison to other methods.


Different algorithms provide different results. There are several algorithms available and you have to choose what suits you best and is right for you. Data compression algorithms are important as they give us many advantages and help us optimize file sizes.

  • You need to buy less storage hardware.
  • You need lower data transmission times.
  • And lower consumption of bandwidth.

Hope this article provides you with the information you need and guides you in the best way possible.

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