Lossy compression

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Original Image (lossless PNG, 60.1 KiB size) — uncompressed is 108.5 KiB
Original Image (lossless PNG, 60.1 KiB size) — uncompressed is 108.5 KiB
Low compression (84% less information than uncompressed PNG, 9.37 KiB)
Low compression (84% less information than uncompressed PNG, 9.37 KiB)
Medium compression (92% less information than uncompressed PNG, 4.82 KiB)
Medium compression (92% less information than uncompressed PNG, 4.82 KiB)
High compression (98% less information than uncompressed PNG, 1.14 KiB)
High compression (98% less information than uncompressed PNG, 1.14 KiB)

A lossy compression method is one where compressing data and then decompressing it retrieves data that may well be different from the original, but is close enough to be useful in some way. Lossy compression is most commonly used to compress multimedia data (audio, video, still images), especially in applications such as streaming media and internet telephony. By contrast, lossless compression is required for text and data files, such as bank records, text articles, etc.

Lossy compression formats suffer from generation loss: repeatedly compressing and decompressing the file will cause it to progressively lose quality. This is in contrast with lossless data compression.

Information-theoretical foundations for lossy data compression are provided by rate-distortion theory. Much like the use of probability in optimal coding theory, rate-distortion theory heavily draws on Bayesian estimation and decision theory in order to model perceptual distortion and even aesthetic judgment.

Contents

Types

There are two basic lossy compression schemes:

  • In lossy transform codecs, samples of picture or sound are taken, chopped into small segments, transformed into a new basis space, and quantized. The resulting quantized values are then entropy coded.
  • In lossy predictive codecs, previous and/or subsequent decoded data is used to predict the current sound sample or image frame. The error between the predicted data and the real data, together with any extra information needed to reproduce the prediction, is then quantized and coded.

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

Lossy versus lossless

The advantage of lossy methods over lossless methods is that in some cases a lossy method can produce a much smaller compressed file than any known lossless method, while still meeting the requirements of the application.

Lossy methods are most often used for compressing sound, images or videos. This is because these types of data are intended for human interpretation where the mind can easily "fill in the blanks" or see past very minor errors or inconsistencies – ideally lossy compression is transparent (imperceptible), which can be verified via an ABX test.

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 bit level while being indistinguishable to the human ear or eye for most practical purposes. Many compression methods focus on the idiosyncrasies of human physiology, taking into account, for instance, that the human eye can see only certain wavelengths of light. 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. 300:1) with little visible quality loss;[citation needed]
  • Audio can often be compressed at 10:1 with imperceptible loss of quality;[citation needed]
  • Still images are often lossily compressed at 10:1, as with audio, but the quality loss is more noticeable, especially on closer inspection.[citation needed]

Transcoding and Editing

For more details on this topic, see Transcoding.

An important caveat about lossy compression is that converting (formally, transcoding) or 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.

Lossless editing

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

Some lossless editing of lossily compressed files is possible, which consists of modifying the compressed data directly, without decoding and re-encoding.

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 one to

JPEGjoin allows one to join different JPEG images (which have the same encoding), without re-encoding. (See also: New jpegtran features.)

One can also make some changes to the compression without re-encoding:

  • optimize the compression (so it takes less space),
  • convert between progressive and non-progressive encoding,

There is also the freeware IrfanView (homepage), which has some lossless JPEG operations in its JPG_TRANSFORM plugin.

MP3

Splitting and joining
Mp3splt and Mp3wrap (or AlbumWrap) allow one to split an MP3 file into pieces or join them loselessly. These are analogous to split and cat.[1]
Gain
Various Replay Gain programs such as MP3gain allow one to modify the gain (overall volume) of MP3 files losslessly.

Metadata

One can generally modify or remove metadata, such as ID3 tags, Vorbis comments, or EXIF information, without modifying the underlying media.

Downsampling / compressed representation scalability

One may wish to downsample or otherwise decrease the resolution of the represented source signal and the quantity of data used for its compressed representation without re-encoding, as in bitrate peeling, but this functionality is not supported in all designs, as not all codecs encode data in a form that allows less important detail to simply be dropped.

Some well known designs that have this capability include JPEG 2000 for still images and H.264/MPEG-4 AVC based Scalable Video Coding for video. Actually such schemes have also been standardized for older designs as well, such as JPEG images with progressive encoding, and MPEG-2 and MPEG-4 Part 2 video, although those prior schemes had limited success in terms of adoption into real-world common usage.

In practice, one often may need to fully decompress a compressed representation and then re-encode it with lower resolution or lower fidelity.

Methods

Graphics

Image

Video

Audio

Music

Speech

Other data

Researchers have (semi-seriously) performed lossy compression on text by either using a thesaurus to substitute short words for long ones, or generative text techniques [2], although these sometimes fall into the related category of lossy data conversion.

See also

Notes

  1. ^ Though the wrap programs do more, encoding the divisions between the original files.
  2. ^ I. H. WITTEN, et al.. "Semantic and Generative Models for Lossy Text Compression" (PDF). The Computer Journal. Retrieved on 2007-10-13.

External links

Retrieved from "http://en.wikipedia.org/wiki/Lossy_compression"

This article is from Wikipedia. All text is available under the terms of the GNU Free Documentation License.

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