Lossless vs. Lossy Data Compression: Key Differences Explained

Data compression has become popular because it reduces the size of a file, which helps save storage space and transmission time. Data compression can be applied to various types of information, including text, images, audio, and video. Applications of data compression are widespread, including generic file compression, multimedia, communication, and database maintenance by companies like Google.

There are two main types of data compression: lossless and lossy. This article will outline the differences between them.

Lossless Data Compression

In lossless data compression, the original data can be exactly restored after decompression. It’s primarily used for text data compression and decompression, but can also be applied to image compression.

Popular lossless algorithms include:

• Run Length Encoding (RLE)
• Huffman Coding
• Adaptive Huffman Coding
• Arithmetic Coding
• Dictionary-based Coding (e.g., LZW)

Run Length Encoding (RLE)

Run Length Encoding is a simple form of data compression. Consecutive runs of identical data are stored as a single data value along with a count of how many times the data repeats.

RLE is particularly useful for compressing simple graphic images like icons and line drawings.

Example:

• Input: ZZZZZZZZZZZZCZZZZZZZZZZZZCCCZZZZZZZZZZZZZZZZZZZZZZZZC
• Output: 12ZC12Z3C24ZC

Huffman Coding

Huffman coding is a well-known and widely used algorithm for data compression. Here’s how it works:

• It assigns a unique bit string to each symbol.
• Symbols that occur more frequently are represented by shorter bit strings.
• Two symbols with the lowest frequencies will have bit strings of the same length, differing only in the last bit.

Adaptive Huffman coding is a variation of the traditional Huffman coding technique, adjusting to changing frequencies as it processes the data.

Adaptive Huffman Coding

Adaptive Huffman coding is adaptive coding technique based on Huffman coding. The encoder and decoder dynamically update the Huffman tree based on the symbols encountered during the compression/decompression process.

Dictionary-based Coding

Techniques like LZ77, LZ78, and LZW use a dynamic dictionary to compress data. LZW is the most popular algorithm in this category.

Arithmetic Coding

Unlike Huffman coding, which assigns a bit string to each symbol, arithmetic coding assigns a unique tag to the entire sequence of data.

Algorithm:

Arithmetic encoding involves representing the entire input data as a single real number within the interval [0, 1). The algorithm iteratively refines the subinterval based on the probabilities of the symbols encountered.

Lossy Data Compression

In lossy data compression, the original data cannot be exactly restored after decompression. The accuracy of the reconstruction is traded for a higher compression ratio.

Lossy compression is primarily used for image and audio data compression and decompression.

Key Characteristics:

• Higher compression ratio compared to lossless methods.
• Some data is permanently lost during compression, leading to a decrease in quality.

Common lossy data compression algorithms include:

• Transform Coding (e.g., Discrete Cosine Transform - DCT)
• Karhunen-Loeve Transform (KLT)
• Wavelet-based Coding (e.g., Continuous Wavelet Transform - CWT and Discrete Wavelet Transform - DWT)

Note: The article lists many different technologies at the end. These are not related to lossless vs lossy data compression, so they were removed.