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Image compression is a type of data compression applied to digital images, to reduce their cost for storage or transmission. Algorithms may take advantage of visual perception and the statistical properties of image data to provide superior results compared with generic compression methods.[1]

Comparison of JPEG images saved by Adobe Photoshop at different quality levels and with or without "save for web"

Lossy and lossless image compression[edit]

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 lossless image compression are:

Methods for lossy compression:

Other properties[edit]

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.[2][3]
  • 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 processing power to encode and decode. Some high compression algorithms require high processing power.

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.

Notes and references[edit]

  1. ^ "Image Data Compression". 
  2. ^ Burt, P.; Adelson, E. (1 April 1983). "The Laplacian Pyramid as a Compact Image Code". IEEE Transactions on Communications. 31 (4): 532–540. CiteSeerX 10.1.1.54.299Freely accessible. doi:10.1109/TCOM.1983.1095851. 
  3. ^ Shao, Dan; Kropatsch, Walter G. (February 3–5, 2010). Špaček, Libor; Franc, Vojtěch, eds. "Irregular Laplacian Graph Pyramid" (PDF). Computer Vision Winter Workshop 2010. Nové Hrady, Czech Republic: Czech Pattern Recognition Society. 

External links[edit]

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