Image compression

Last updated

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 data compression methods which are used for other digital data. [1]

Contents

Comparison of JPEG images saved by Adobe Photoshop at different quality levels and with or without "save for web" Quality comparison jpg vs saveforweb.jpg
Comparison of JPEG images saved by Adobe Photoshop at different quality levels and with or without "save for web"

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:

Methods for lossless compression:

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:

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.

History

Entropy coding started in the 1940s with the introduction of Shannon–Fano coding, [5] the basis for Huffman coding which was developed in 1950. [6] 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. [7]

An important development in image data compression was the discrete cosine transform (DCT), a lossy compression technique first proposed by Nasir Ahmed in 1972. [8] DCT compression became the basis for JPEG, which was introduced by the Joint Photographic Experts Group (JPEG) in 1992. [9] JPEG compresses images down to much smaller file sizes, and has become the most widely used image file format. [10] Its highly efficient DCT compression algorithm was largely responsible for the wide proliferation of digital images and digital photos, [11] with several billion JPEG images produced every day as of 2015. [12]

Lempel–Ziv–Welch (LZW) is a lossless compression algorithm developed by Abraham Lempel, Jacob Ziv and Terry Welch in 1984. It is used in the GIF format, introduced in 1987. [13] DEFLATE, a lossless compression algorithm developed by Phil Katz and specified in 1996, is used in the Portable Network Graphics (PNG) format. [14]

Wavelet coding, the use of wavelet transforms in image compression, began after the development of DCT coding. [15] The introduction of the DCT led to the development of wavelet coding, a variant of DCT coding that uses wavelets instead of DCT's block-based algorithm. [15] The JPEG 2000 standard was developed from 1997 to 2000 by a JPEG committee chaired by Touradj Ebrahimi (later the JPEG president). [16] In contrast to the DCT algorithm used by the original JPEG format, JPEG 2000 instead uses discrete wavelet transform (DWT) algorithms. It uses the CDF 9/7 wavelet transform (developed by Ingrid Daubechies in 1992) for its lossy compression algorithm, [17] and the LeGall-Tabatabai (LGT) 5/3 wavelet transform [18] [19] (developed by Didier Le Gall and Ali J. Tabatabai in 1988) [20] for its lossless compression algorithm. [17] JPEG 2000 technology, which includes the Motion JPEG 2000 extension, was selected as the video coding standard for digital cinema in 2004. [21]

Notes and references

  1. "Image Data Compression".
  2. Nasir Ahmed, T. Natarajan and K. R. Rao, "Discrete Cosine Transform," IEEE Trans. Computers, 90–93, Jan. 1974.
  3. Burt, P.; Adelson, E. (1 April 1983). "The Laplacian Pyramid as a Compact Image Code". IEEE Transactions on Communications. 31 (4): 532–540. CiteSeerX   10.1.1.54.299 . doi:10.1109/TCOM.1983.1095851.
  4. 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.
  5. Claude Elwood Shannon (1948). Alcatel-Lucent (ed.). "A Mathematical Theory of Communication" (PDF). Bell System Technical Journal. 27 (3–4): 379–423, 623–656. Retrieved 2019-04-21.
  6. David Albert Huffman (September 1952), "A method for the construction of minimum-redundancy codes" (PDF), Proceedings of the IRE , 40 (9), pp. 1098–1101, doi:10.1109/JRPROC.1952.273898
  7. William K. Pratt, Julius Kane, Harry C. Andrews: "Hadamard transform image coding", in Proceedings of the IEEE 57.1 (1969): Seiten 58–68
  8. Ahmed, Nasir (January 1991). "How I Came Up With the Discrete Cosine Transform". Digital Signal Processing . 1 (1): 4–5. doi:10.1016/1051-2004(91)90086-Z.
  9. "T.81 – DIGITAL COMPRESSION AND CODING OF CONTINUOUS-TONE STILL IMAGES – REQUIREMENTS AND GUIDELINES" (PDF). CCITT. September 1992. Retrieved 12 July 2019.
  10. "The JPEG image format explained". BT.com . BT Group. 31 May 2018. Retrieved 5 August 2019.
  11. "What Is a JPEG? The Invisible Object You See Every Day". The Atlantic . 24 September 2013. Retrieved 13 September 2019.
  12. Baraniuk, Chris (15 October 2015). "Copy protections could come to JPEGs". BBC News . BBC . Retrieved 13 September 2019.
  13. "The GIF Controversy: A Software Developer's Perspective" . Retrieved 26 May 2015.
  14. L. Peter Deutsch (May 1996). DEFLATE Compressed Data Format Specification version 1.3. IETF. p. 1. sec. Abstract. doi: 10.17487/RFC1951 . RFC 1951 . Retrieved 2014-04-23.
  15. 1 2 Hoffman, Roy (2012). Data Compression in Digital Systems. Springer Science & Business Media. p. 124. ISBN   9781461560319. Basically, wavelet coding is a variant on DCT-based transform coding that reduces or eliminates some of its limitations. (...) Another advantage is that rather than working with 8 × 8 blocks of pixels, as do JPEG and other block-based DCT techniques, wavelet coding can simultaneously compress the entire image.
  16. Taubman, David; Marcellin, Michael (2012). JPEG2000 Image Compression Fundamentals, Standards and Practice: Image Compression Fundamentals, Standards and Practice. Springer Science & Business Media. ISBN   9781461507994.
  17. 1 2 Unser, M.; Blu, T. (2003). "Mathematical properties of the JPEG2000 wavelet filters" (PDF). IEEE Transactions on Image Processing. 12 (9): 1080–1090. Bibcode:2003ITIP...12.1080U. doi:10.1109/TIP.2003.812329. PMID   18237979. S2CID   2765169.
  18. Sullivan, Gary (8–12 December 2003). "General characteristics and design considerations for temporal subband video coding". ITU-T . Video Coding Experts Group . Retrieved 13 September 2019.CS1 maint: date format (link)
  19. Bovik, Alan C. (2009). The Essential Guide to Video Processing. Academic Press. p. 355. ISBN   9780080922508.
  20. Gall, Didier Le; Tabatabai, Ali J. (1988). "Sub-band coding of digital images using symmetric short kernel filters and arithmetic coding techniques". ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing: 761–764 vol.2. doi:10.1109/ICASSP.1988.196696. S2CID   109186495.
  21. Swartz, Charles S. (2005). Understanding Digital Cinema: A Professional Handbook. Taylor & Francis. p. 147. ISBN   9780240806174.

Related Research Articles

In signal processing, data compression, source coding, or bit-rate reduction is the process of encoding information using fewer bits than the original representation. Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by removing unnecessary or less important information. Typically, a device that performs data compression is referred to as an encoder, and one that performs the reversal of the process (decompression) as a decoder.

JPEG Lossy compression method for digital images

JPEG is a commonly used method of lossy compression for digital images, particularly for those images produced by digital photography. The degree of compression can be adjusted, allowing a selectable tradeoff between storage size and image quality. JPEG typically achieves 10:1 compression with little perceptible loss in image quality. Since its introduction in 1992, JPEG has been the most widely used image compression standard in the world, and the most widely used digital image format, with several billion JPEG images produced every day as of 2015.

Lossy compression data compression approach that reduces data size while discarding or changing some of it

In information technology, lossy compression or irreversible compression is the class of data encoding methods that uses inexact approximations and partial data discarding to represent the content. These techniques are used to reduce data size for storing, handling, and transmitting content. The different versions of the photo of the cat to the right show how higher degrees of approximation create coarser images as more details are removed. This is opposed to lossless data compression which does not degrade the data. The amount of data reduction possible using lossy compression is much higher than through lossless techniques.

Lossless compression is a class of data compression algorithms that allows the original data to be perfectly reconstructed from the compressed data. By contrast, lossy compression permits reconstruction only of an approximation of the original data, though usually with greatly improved compression rates.

Transform coding is a type of data compression for "natural" data like audio signals or photographic images. The transformation is typically lossless on its own but is used to enable better quantization, which then results in a lower quality copy of the original input.

A video codec is software or hardware that compresses and decompresses digital video. In the context of video compression, codec is a portmanteau of encoder and decoder, while a device that only compresses is typically called an encoder, and one that only decompresses is a decoder.

A discrete cosine transform (DCT) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. The DCT, first proposed by Nasir Ahmed in 1972, is a widely used transformation technique in signal processing and data compression. It is used in most digital media, including digital images, digital video, digital audio, digital television, digital radio, and speech coding. DCTs are also important to numerous other applications in science and engineering, such as digital signal processing, communications devices, reducing network bandwidth usage, and spectral methods for the numerical solution of partial differential equations.

JPEG 2000 Image compression standard and coding system

JPEG 2000 (JP2) is an image compression standard and coding system. It was developed from 1997 to 2000 by a Joint Photographic Experts Group committee chaired by Touradj Ebrahimi, with the intention of superseding their original discrete cosine transform (DCT) based JPEG standard with a newly designed, wavelet-based method. The standardized filename extension is .jp2 for ISO/IEC 15444-1 conforming files and .jpx for the extended part-2 specifications, published as ISO/IEC 15444-2. The registered MIME types are defined in RFC 3745. For ISO/IEC 15444-1 it is image/jp2.

Compression artifact noticeable distortion of media caused by the application of lossy data compression

A compression artifact is a noticeable distortion of media caused by the application of lossy compression. Lossy data compression involves discarding some of the media's data so that it becomes small enough to be stored within the desired disk space or transmitted (streamed) within the available bandwidth. If the compressor cannot store enough data in the compressed version, the result is a loss of quality, or introduction of artifacts. The compression algorithm may not be intelligent enough to discriminate between distortions of little subjective importance and those objectionable to the user.

ICER is a wavelet-based image compression file format used by the NASA Mars Rovers. ICER has both lossy and lossless compression modes.

Image file formats are standardized means of organizing and storing digital images. An image file format may store data in an uncompressed format, a compressed format, or a vector format. Image files are composed of digital data in one of these formats so that the data can be rasterized for use on a computer display or printer. Rasterization converts the image data into a grid of pixels. Each pixel has a number of bits to designate its color. Rasterizing an image file for a specific device takes into account the number of bits per pixel that the device is designed to handle.

Wavelet transform mathematical technique used in data compression and analysis

In mathematics, a wavelet series is a representation of a square-integrable function by a certain orthonormal series generated by a wavelet. This article provides a formal, mathematical definition of an orthonormal wavelet and of the integral wavelet transform.

Lossless JPEG is a 1993 addition to JPEG standard by the Joint Photographic Experts Group to enable lossless compression. However, the term may also be used to refer to all lossless compression schemes developed by the group, including JPEG 2000 and JPEG-LS.

JPEG XR is a still-image compression standard and file format for continuous tone photographic images, based on technology originally developed and patented by Microsoft under the name HD Photo. It supports both lossy and lossless compression, and is the preferred image format for Ecma-388 Open XML Paper Specification documents.

Progressive Graphics File file format

PGF is a wavelet-based bitmapped image format that employs lossless and lossy data compression. PGF was created to improve upon and replace the JPEG format. It was developed at the same time as JPEG 2000 but with a focus on speed over compression ratio.

A video coding format is a content representation format for storage or transmission of digital video content. It typically uses a standardized video compression algorithm, most commonly based on discrete cosine transform (DCT) coding and motion compensation. Examples of video coding formats include H.262, MPEG-4 Part 2, H.264, HEVC (H.265), Theora, RealVideo RV40, VP9, and AV1. A specific software or hardware implementation capable of compression or decompression to/from a specific video coding format is called a video codec; an example of a video codec is Xvid, which is one of several different codecs which implements encoding and decoding videos in the MPEG-4 Part 2 video coding format in software.

Nasir Ahmed (engineer) Professor Emeritus of Electrical and Computer and Engineering at University of New Mexico (UNM)

Nasir Ahmed is an Indian-American electrical engineer and computer scientist. He is Professor Emeritus of Electrical and Computer Engineering at University of New Mexico (UNM). He is best known for inventing the discrete cosine transform (DCT) in the early 1970s. The DCT is the most widely used data compression transformation, the basis for most digital media standards and commonly used in digital signal processing. He also described the discrete sine transform (DST), which is related to the DCT.

Audio coding format Digitally coded format for audio signals

An audio coding format is a content representation format for storage or transmission of digital audio. Examples of audio coding formats include MP3, AAC, Vorbis, FLAC, and Opus. A specific software or hardware implementation capable of audio compression and decompression to/from a specific audio coding format is called an audio codec; an example of an audio codec is LAME, which is one of several different codecs which implements encoding and decoding audio in the MP3 audio coding format in software.

ZPEG is a motion video technology that applies a human visual acuity model to a connected frequency, thereby removing the superfluous which is unnoticeable. This technology is applicable to a wide range of video processing problems such as video optimization, real-time motion video compression, subjective quality monitoring, and format conversion.

JPEG XT is an image compression standard which specifies backward-compatible extensions of the base JPEG standard.