Image compression

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Image compression is a type of

Lossy and lossless image compression

Image compression may be lossy or lossless. Lossless compression is preferred for archival purposes and often for medical imaging, technical drawings, clip art, or comics. Lossy compression methods, especially when used at low bit rates, introduce compression artifacts. Lossy methods are especially suitable for natural images such as photographs in applications where minor (sometimes imperceptible) loss of fidelity is acceptable to achieve a substantial reduction in bit rate. Lossy compression that produces negligible differences may be called visually lossless.

Methods for lossy compression:

• Transform coding – This is the most commonly used method.
  • Nasir Ahmed, T. Natarajan and K. R. Rao in 1974.^[2] The DCT is sometimes referred to as "DCT-II" in the context of a family of discrete cosine transforms (see discrete cosine transform
    ). It is generally the most efficient form of image compression.
    • DCT is used in
      HEIF
      .
  • The more recently developed wavelet transform is also used extensively, followed by quantization and entropy coding.
• dithering to avoid posterization
  .
  • Whole-image palette, typically 256 colors, used in GIF and PNG file formats.
  • block palette, typically 2 or 4 colors for each block of 4x4 pixels, used in BTC, CCC, S2TC, and S3TC.
• Chroma subsampling. This takes advantage of the fact that the human eye perceives spatial changes of brightness more sharply than those of color, by averaging or dropping some of the chrominance information in the image.
• Fractal compression.
• More recently, methods based on
  Machine Learning were applied, using Multilayer perceptrons, Convolutional neural networks and Generative adversarial networks.^[3] Implementations are available in OpenCV, TensorFlow, MATLAB's Image Processing Toolbox (IPT), and the High-Fidelity Generative Image Compression (HiFiC) open source project.^[4]

Methods for lossless compression:

• TGA, TIFF
• Area image compression
• Predictive coding – used in
  DPCM
• Entropy encoding – the two most common entropy encoding techniques are arithmetic coding and Huffman coding
• Adaptive dictionary algorithms such as
  GIF and TIFF
• PNG, MNG, and TIFF
• Chain codes
• Diffusion models^[5]

Other properties

The best image quality at a given compression rate (or bit rate) is the main goal of image compression, however, there are other important properties of image compression schemes:

Scalability generally refers to a quality reduction achieved by manipulation of the bitstream or file (without decompression and re-compression). Other names for scalability are progressive coding or embedded bitstreams. Despite its contrary nature, scalability also may be found in lossless codecs, usually in form of coarse-to-fine pixel scans. Scalability is especially useful for previewing images while downloading them (e.g., in a web browser) or for providing variable quality access to e.g., databases. There are several types of scalability:

• Quality progressive or layer progressive: The bitstream successively refines the reconstructed image.
• Resolution progressive: First encode a lower image resolution; then encode the difference to higher resolutions.^[6][7]
• Component progressive: First encode grey-scale version; then adding full color.

Region of interest coding. Certain parts of the image are encoded with higher quality than others. This may be combined with scalability (encode these parts first, others later).

Meta information. Compressed data may contain information about the image which may be used to categorize, search, or browse images. Such information may include color and texture statistics, small preview images, and author or copyright information.

Processing power. Compression algorithms require different amounts of

The quality of a compression method often is measured by the peak signal-to-noise ratio. It measures the amount of noise introduced through a lossy compression of the image, however, the subjective judgment of the viewer also is regarded as an important measure, perhaps, being the most important measure.

History

Entropy coding started in the late 1940s with the introduction of Shannon–Fano coding,^[8] the basis for Huffman coding which was published in 1952.^[9] Transform coding dates back to the late 1960s, with the introduction of fast Fourier transform (FFT) coding in 1968 and the Hadamard transform in 1969.^[10]

An important development in image