Lossy compression

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Low-compression (high quality) JPEG
High-compression (low quality) JPEG

In information technology, lossy compression or irreversible compression is the class of data compression 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 on this page show how higher degrees of approximation create coarser images as more details are removed. This is opposed to lossless data compression (reversible data compression) which does not degrade the data. The amount of data reduction possible using lossy compression is much higher than using lossless techniques.

Well-designed lossy compression technology often reduces file sizes significantly before degradation is noticed by the end-user. Even when noticeable by the user, further data reduction may be desirable (e.g., for real-time communication or to reduce transmission times or storage needs). The most widely used lossy compression algorithm is the

Nasir Ahmed, T. Natarajan and K. R. Rao
in 1974.

Lossy compression is most commonly used to compress

master lossless file which can then be used to produce additional copies from. This allows one to avoid basing new compressed copies off of a lossy source file, which would yield additional artifacts and further unnecessary information loss
.

Types

It is possible to compress many types of digital data in a way that reduces the size of a computer file needed to store it, or the bandwidth needed to transmit it, with no loss of the full information contained in the original file. A picture, for example, is converted to a digital file by considering it to be an array of dots and specifying the color and brightness of each dot. If the picture contains an area of the same color, it can be compressed without loss by saying "200 red dots" instead of "red dot, red dot, ...(197 more times)..., red dot."

The original data contains a certain amount of information, and there is a lower bound to the size of a file that can still carry all the information. Basic information theory says that there is an absolute limit in reducing the size of this data. When data is compressed, its entropy increases, and it cannot increase indefinitely. For example, a compressed ZIP file is smaller than its original, but repeatedly compressing the same file will not reduce the size to nothing. Most compression algorithms can recognize when further compression would be pointless and would in fact increase the size of the data.

In many cases, files or data streams contain more information than is needed. For example, a picture may have more detail than the eye can distinguish when reproduced at the largest size intended; likewise, an audio file does not need a lot of fine detail during a very loud passage. Developing lossy compression techniques as closely matched to human perception as possible is a complex task. Sometimes the ideal is a file that provides exactly the same perception as the original, with as much digital information as possible removed; other times, perceptible loss of quality is considered a valid tradeoff.

The terms "irreversible" and "reversible" are preferred over "lossy" and "lossless" respectively for some applications, such as medical image compression, to circumvent the negative implications of "loss". The type and amount of loss can affect the utility of the images. Artifacts or undesirable effects of compression may be clearly discernible yet the result still useful for the intended purpose. Or lossy compressed images may be '

visually lossless', or in the case of medical images, so-called diagnostically acceptable irreversible compression (DAIC)[1]
may have been applied.

Transform coding

Main article: Transform coding

Some forms of lossy compression can be thought of as an application of

signals, and digital video. The transformation is typically used to enable better (more targeted) quantization. Knowledge of the application is used to choose information to discard, thereby lowering its bandwidth
. The remaining information can then be compressed via a variety of methods. When the output is decoded, the result may not be identical to the original input, but is expected to be close enough for the purpose of the application.

The most common form of lossy compression is a transform coding method, the

AAC
).

In the case of audio data, a popular form of transform coding is

perceptual coding, which transforms the raw data to a domain that more accurately reflects the information content. For example, rather than expressing a sound file as the amplitude levels over time, one may express it as the frequency spectrum over time, which corresponds more accurately to human audio perception. While data reduction (compression, be it lossy or lossless) is a main goal of transform coding, it also allows other goals: one may represent data more accurately for the original amount of space[5] – for example, in principle, if one starts with an analog or high-resolution digital master, an MP3 file of a given size should provide a better representation than a raw uncompressed audio in WAV or AIFF file of the same size. This is because uncompressed audio can only reduce file size by lowering bit rate or depth, whereas compressing audio can reduce size while maintaining bit rate and depth. This compression becomes a selective loss of the least significant data, rather than losing data across the board. Further, a transform coding may provide a better domain for manipulating or otherwise editing the data – for example, equalization
of audio is most naturally expressed in the frequency domain (boost the bass, for instance) rather than in the raw time domain.

From this point of view, perceptual encoding is not essentially about discarding data, but rather about a better representation of data. Another use is for

scanline
, 150 pixels of yellow vs. green, and 50 pixels of blue vs. red, which are proportional to human sensitivity to each component.

Information loss

Lossy compression formats suffer from

aesthetic
judgment.

There are two basic lossy compression schemes:

In some systems the two techniques are combined, with transform codecs being used to compress the error signals generated by the predictive stage.

Comparison

The advantage of lossy methods over

streaming audio services such as Spotify
.

Emotional effects

A study conducted by the Audio Engineering Library concluded that lower bit rate (112 kbps) lossy compression formats such as MP3s have distinct effects on timbral and emotional characteristics, tending to strengthen negative emotional qualities and weaken positive ones.[6] The study further noted that the trumpet is the instrument most affected by compression, while the horn is least.

Transparency

Further information: Transparency (data compression)

When a user acquires a lossily compressed file, (for example, to reduce download time) the retrieved file can be quite different from the original at the

psychoacoustic model describes how sound can be highly compressed without degrading perceived quality. Flaws caused by lossy compression that are noticeable to the human eye or ear are known as compression artifacts
.

Compression ratio

The compression ratio (that is, the size of the compressed file compared to that of the uncompressed file) of lossy video codecs is nearly always far superior to that of the audio and still-image equivalents.

  • Video can be compressed immensely (e.g., 100:1) with little visible quality loss
  • Audio can often be compressed at 10:1 with almost imperceptible loss of quality
  • Still images are often lossily compressed at 10:1, as with audio, but the quality loss is more noticeable, especially on closer inspection.

Transcoding and editing

Further information: Transcoding

An important caveat about lossy compression (formally transcoding), is that editing lossily compressed files causes

digital generation loss from the re-encoding. This can be avoided by only producing lossy files from (lossless) originals and only editing (copies of) original files, such as images in raw image format instead of JPEG. If data which has been compressed lossily is decoded and compressed losslessly, the size of the result can be comparable with the size of the data before lossy compression, but the data already lost cannot be recovered. When deciding to use lossy conversion without keeping the original, format conversion may be needed in the future to achieve compatibility with software or devices (format shifting), or to avoid paying patent royalties
for decoding or distribution of compressed files.

Editing of lossy files

See also: commons:Commons:Software § JPEG, and commons:Commons:Software § Ogg Vorbis (audio)

By modifying the compressed data directly without decoding and re-encoding, some editing of lossily compressed files without degradation of quality is possible. Editing which reduces the file size as if it had been compressed to a greater degree, but without more loss than this, is sometimes also possible.

JPEG

The primary programs for lossless editing of JPEGs are

jpegtran, and the derived exiftran (which also preserves Exif information), and Jpegcrop
(which provides a Windows interface).

These allow the image to be cropped, rotated, flipped, and flopped, or even converted to grayscale (by dropping the chrominance channel). While unwanted information is destroyed, the quality of the remaining portion is unchanged.

Some other transforms are possible to some extent, such as joining images with the same encoding (composing side by side, as on a grid) or pasting images such as logos onto existing images (both via Jpegjoin), or scaling.[7]

Some changes can be made to the compression without re-encoding:

  • Optimizing the compression (to reduce size without change to the decoded image)
  • Converting between progressive and non-progressive encoding.

The freeware Windows-only IrfanView has some lossless JPEG operations in its JPG_TRANSFORM plugin.

Metadata

Metadata, such as

ID3 tags, Vorbis comments, or Exif
information, can usually be modified or removed without modifying the underlying data.

Downsampling/compressed representation scalability

One may wish to

digital generation loss
.

Another approach is to encode the original signal at several different bitrates, and then either choose which to use (as when streaming over the internet – as in

OptimFROG DualStream, and DTS-HD Master Audio in lossless (XLL) mode
).

Methods

Graphics

Image

Further information: Image compression

3D computer graphics

Video

Further information:
Video compression

Audio

Further information:
Audio data compression

General

Speech

Further information:
Speech encoding

Other data

Researchers have performed lossy compression on text by either using a thesaurus to substitute short words for long ones, or generative text techniques,[15] although these sometimes fall into the related category of lossy data conversion.

Lowering resolution

A general kind of lossy compression is to lower the resolution of an image, as in

decimation. One may also remove less "lower information" parts of an image, such as by seam carving. Many media transforms, such as Gaussian blur, are, like lossy compression, irreversible: the original signal cannot be reconstructed from the transformed signal. However, in general these will have the same size as the original, and are not a form of compression. Lowering resolution has practical uses, as the NASA New Horizons craft transmitted thumbnails of its encounter with Pluto-Charon before it sent the higher resolution images. Another solution for slow connections is the usage of Image interlacing which progressively defines the image. Thus a partial transmission is enough to preview the final image, in a lower resolution version, without creating a scaled and a full version too.[citation needed
]

See also

Notes

  1. PMID 22347940
    .
  2. Encyclopedia Britannica
    . Retrieved 13 August 2019.
  3. S2CID 149806273
  4. ^ "T.81 – DIGITAL COMPRESSION AND CODING OF CONTINUOUS-TONE STILL IMAGES – REQUIREMENTS AND GUIDELINES" (PDF). CCITT. September 1992. Retrieved 12 July 2019.
  5. ^ “Although one main goal of digital audio perceptual coders is data reduction, this is not a necessary characteristic. As we shall see, perceptual coding can be used to improve the representation of digital audio through advanced bit allocation.” Masking and Perceptual Coding, Victor Lombardi, noisebetweenstations.com
  6. ^ Svetlik, Joe (December 5, 2016). "MP3s make you less happy, study says". What Hi Fi?. Retrieved December 17, 2018.
  7. ^ "New jpegtran features". sylvana.net. Retrieved 2019-09-20.
  8. ^ a b c d e f Stanković, Radomir S.; Astola, Jaakko T. (2012). "Reminiscences of the Early Work in DCT: Interview with K.R. Rao" (PDF). Reprints from the Early Days of Information Sciences. 60. Retrieved 13 October 2019.
  9. ^ a b K. R. Rao and J. J. Hwang, Techniques and Standards for Image, Video, and Audio Coding, Prentice Hall, 1996; JPEG: Chapter 8; H.261: Chapter 9; MPEG-1: Chapter 10; MPEG-2: Chapter 11.
  10. ^ Guckert, John (Spring 2012). "The Use of FFT and MDCT in MP3 Audio Compression" (PDF). University of Utah. Retrieved 14 July 2019.
  11. ^ Brandenburg, Karlheinz (1999). "MP3 and AAC Explained" (PDF). Archived (PDF) from the original on 2017-02-13.
  12. ^ Darko, John H. (2017-03-29). "The inconvenient truth about Bluetooth audio". DAR__KO. Archived from the original on 2018-01-14. Retrieved 2018-01-13.
  13. ^ Ford, Jez (2015-08-24). "What is Sony LDAC, and how does it do it?". AVHub. Retrieved 2018-01-13.
  14. ^ Ford, Jez (2016-11-22). "aptX HD - lossless or lossy?". AVHub. Retrieved 2018-01-13.
  15. ^ I. H. WITTEN; et al. "Semantic and Generative Models for Lossy Text Compression" (PDF). The Computer Journal. Retrieved 2007-10-13.

External links

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