Independent Seminar Report Saurabh

Independent Seminar Report

Topic – Data Compression

Submitted to- Submitted by-

Miss Pooja Chauhan Saurabh

(HOD- ECE) ece/06/144

Introduction In computer science and information theory, data compression or source coding is the process of encoding information using fewer bits (or other information-bearing units) than an unencodedrepresentation would use, through use of specific encoding schemes.

As with any communication, compressed data communication only works when both

the sender and receiver of the information understand the encoding scheme. For example, this

text makes sense only if the receiver understands that it is intended to be interpreted as characters

representing the English language. Similarly, compressed data can only be understood if the

decoding method is known by the receiver.

Compression is useful because it helps reduce the consumption of expensive resources, such

as hard disk space or transmission bandwidth. On the downside, compressed data must be

decompressed to be used, and this extra processing may be detrimental to some applications. For

instance, a compression scheme for video may require expensive hardware for the video to be

decompressed fast enough to be viewed as it's being decompressed (the option of decompressing

the video in full before watching it may be inconvenient, and requires storage space for the

decompressed video). The design of data compression schemes therefore involves trade-offs

among various factors, including the degree of compression, the amount of distortion introduced

(if using a lossy compression scheme), and the computational resources required to compress and

uncompress the data.

Lossless compressionLossless data compression is a class of data compression algorithms that allows the exact

original data to be reconstructed from the compressed data. The term lossless is in contrast

to lossy data compression, which only allows an approximation of the original data to be

reconstructed in exchange for better compression rates.

Lossless data compression is used in many applications. For example, it is used in the

popular ZIP file format and in the Unix tool gzip. It is also often used as a component within

lossy data compression technologies.

Lossless compression is used when it is important that the original and the decompressed data be

identical, or when no assumption can be made on whether certain deviation is uncritical. Typical

examples are executable programs and source code. Some image file formats, like PNG or GIF,

use only lossless compression, while others like TIFF and MNG may use either lossless or lossy

methods.

Most lossless compression programs do two things in sequence: the first step generates

a statistical model for the input data, and the second step uses this model to map input data to bit

sequences in such a way that "probable" (e.g. frequently encountered) data will produce shorter

output than "improbable" data.

The primary encoding algorithms used to produce bit sequences are Huffman coding (also used

by DEFLATE) and arithmetic coding. Arithmetic coding achieves compression rates close to the

best possible for a particular statistical model, which is given by the information entropy,

whereas Huffman compression is simpler and faster but produces poor results for models that

deal with symbol probabilities close to 1.

There are two primary ways of constructing statistical models: in a static model, the data are

analyzed and a model is constructed, then this model is stored with the compressed data. This

approach is simple and modular, but has the disadvantage that the model itself can be expensive

to store, and also that it forces a single model to be used for all data being compressed, and so

performs poorly on files containing heterogeneous data. Adaptive models dynamically update the

model as the data are compressed. Both the encoder and decoder begin with a trivial model,

yielding poor compression of initial data, but as they learn more about the data performance

improves. Most popular types of compression used in practice now use adaptive coders.

Lossless compression methods may be categorized according to the type of data they are

designed to compress. While, in principle, any general-purpose lossless compression algorithm

(general-purposemeaning that they can compress any bitstring) can be used on any type of data,

many are unable to achieve significant compression on data that are not of the form for which

they were designed to compress. Many of the lossless compression techniques used for text also

work reasonably well for indexed images.

Lossy compressionA lossy compression method is one where compressing dataand then decompressing it retrieves

data that is different from the original, but is close enough to be useful in some way. Lossy

compression is most commonly used to compress multimediadata (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. In many

cases it is advantageous to make a master lossless file which can then be used to produce

compressed files for different purposes; for example a multi-megabyte file can be used at full

size to produce a full-page advertisement in a glossy magazine, and a 10 kilobyte lossy copy

made for a small image on a web page.

Original Image (lossless PNG, 60.1 KBsize) — uncompressed is 108.5 KB

Low compression (84% less information than uncompressed PNG, 9.37 KB)

Medium compression (92% less information than uncompressed PNG, 4.82 KB)

High compression (98% less information than uncompressed PNG, 1.14 KB)

It is possible to compress many types of digital data in a way which reduces the amount of

information stored, and consequently the size of a computer file needed to store it or

thebandwidth needed to stream it, with no loss of information. A picture, for example, is

converted to a digital file by considering it to be an array of dots, and specifying the colour and

brightness of each dot. If the picture contains an area of the same colour, it can be compressed

without loss by saying "200 red dots" instead of "red dot, red dot, ...(197 more times)..., red dot".

The original contains a certain amount of information; there is a lower limit to the size of file that

can carry all the information. As an intuitive example, most people know that a compressed ZIP

file is smaller than the original file; but repeatedly compressing the file will not reduce the size to

nothing, and will in fact usually increase the size.

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; 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. In some cases the ideal is a file which provides exactly the same perception as the original,

with as much digital information as possible removed; in other cases perceptible loss of quality is

considered a valid trade-off for the reduced data size.

Lossless versus lossy compressionLossless compression algorithms usually exploit statistical redundancy in such a way as to

represent the sender's data more concisely without error. Lossless compression is possible

because most real-world data has statistical redundancy. For example, in English text, the letter

'e' is much more common than the letter 'z', and the probability that the letter 'q' will be followed

by the letter 'z' is very small. Another kind of compression, called lossy data

compression or perceptual coding, is possible if some loss of fidelity is acceptable. Generally, a

lossy data compression will be guided by research on how people perceive the data in question.

For example, the human eye is more sensitive to subtle variations in luminance than it is to

variations in color. JPEG image compression works in part by "rounding off" some of this less-

important information. Lossy data compression provides a way to obtain the best fidelity for a

given amount of compression. In some cases, transparent (unnoticeable) compression is desired;

in other cases, fidelity is sacrificed to reduce the amount of data as much as possible.

Lossless compression schemes are reversible so that the original data can be reconstructed, while

lossy schemes accept some loss of data in order to achieve higher compression.

However, lossless data compression algorithms will always fail to compress some files; indeed,

any compression algorithm will necessarily fail to compress any data containing no discernible

patterns. Attempts to compress data that has been compressed already will therefore usually

result in an expansion, as will attempts to compress all but the most trivially encrypted data.

In practice, lossy data compression will also come to a point where compressing again does not

work, although an extremely lossy algorithm, like for example always removing the last byte of a

file, will always compress a file up to the point where it is empty.

An example of lossless vs. lossy compression is the following string:

25.888888888

This string can be compressed as:

25.[9]8

Interpreted as, "twenty five point 9 eights", the original string is perfectly recreated,

just written in a smaller form. In a lossy system, using

26

instead, the exact original data is lost, at the benefit of a smaller file size.

Audio compression (data)Audio compression is a form of data compression designed to reduce the transmission

bandwidth requirement of digital audio streams and the storage size of audio files. Audio

compression algorithmsare implemented in computer software as audio codecs. Generic data

compression algorithms perform poorly with audio data, seldom reducing data size much below

87% from the original,[citation needed] and are not designed for use in real time applications.

Consequently, specifically optimized audio losslessand lossy algorithms have been created.

Lossy algorithms provide greater compression rates and are used in mainstream consumer audio

devices.

In both lossy and lossless compression, information redundancy is reduced, using methods such

ascoding, pattern recognition and linear prediction to reduce the amount of information used to

represent the uncompressed data.

The trade-off between slightly reduced audio quality and transmission or storage size is

outweighed by the latter for most practical audio applications in which users may not perceive

the loss in playback rendition quality. For example, one compact disk (CD) holds approximately

one hour of uncompressed high fidelity music, less than 2 hours of music compressed losslessly,

or 7 hours of music compressed in the MP3 format at medium bit rates.

FormatsShorten was an early lossless format; newer ones include Free Lossless Audio

Codec (FLAC), Apple's Apple Lossless , MPEG-4 ALS, Monkey's Audio, and TTA.

Some audio formats feature a combination of a lossy format and a lossless correction; this allows

stripping the correction to easily obtain a lossy file. Such formats include MPEG-4

SLS (Scalable to Lossless), WavPack, and OptimFROG DualStream.

Some formats are associated with a technology, such as:

Direct Stream Transfer , used in Super Audio CD

Meridian Lossless Packing , used in DVD-Audio, Dolby TrueHD, Blu-ray and HD DVD

Difficulties in lossless compression of audio dataIt is difficult to maintain all the data in an audio stream and achieve substantial compression.

First, the vast majority of sound recordings are highly complex, recorded from the real world. As

one of the key methods of compression is to find patterns and repetition, more chaotic data such

as audio doesn't compress well. In a similar manner, photographs compress less efficiently with

lossless methods than simpler computer-generated images do. But interestingly, even computer

generated sounds can contain very complicated waveforms that present a challenge to many

compression algorithms. This is due to the nature of audio waveforms, which are generally

difficult to simplify without a (necessarily lossy) conversion to frequency information, as

performed by the human ear.

The second reason is that values of audio samples change very quickly, so generic data

compressionalgorithms don't work well for audio, and strings of consecutive bytes don't

generally appear very often. However, convolution with the filter [-1 1] (that is, taking the first

derivative) tends to slightly whiten(decorrelate, make flat) the spectrum, thereby allowing

traditional lossless compression at the encoder to do its job; integration at the decoder restores

the original signal. Codecs such as FLAC, Shorten andTTA use linear prediction to estimate the

spectrum of the signal. At the encoder, the estimator's inverse is used to whiten the signal by

removing spectral peaks while the estimator is used to reconstruct the original signal at the

decoder.

Evaluation criteriaLossless audio codecs have no quality issues, so the usability can be estimated by

Speed of compression and decompression

Degree of compression

Robustness and error correction

Product support

Lossy audio compressionLossy audio compression is used in a wide range of applications. In addition to the direct

applications (mp3 players or computers), digitally compressed audio streams are used in most

video DVDs; digital television; streaming media on the internet; satellite and cable radio; and

increasingly in terrestrial radio broadcasts. Lossy compression typically achieves far greater

compression than lossless compression (data of 5 percent to 20 percent of the original stream,

rather than 50 percent to 60 percent), by discarding less-critical data.

The innovation of lossy audio compression was to use psychoacoustics to recognize that not all

data in an audio stream can be perceived by the human auditory system. Most lossy compression

reduces perceptual redundancy by first identifying sounds which are considered perceptually

irrelevant, that is, sounds that are very hard to hear. Typical examples include high frequencies,

or sounds that occur at the same time as louder sounds. Those sounds are coded with decreased

accuracy or not coded at all.

While removing or reducing these 'unhearable' sounds may account for a small percentage of bits

saved in lossy compression, the real savings comes from a complementary phenomenon: noise

shaping. Reducing the number of bits used to code a signal increases the amount of noise in that

signal. In psychoacoustics-based lossy compression, the real key is to 'hide' the noise generated

by the bit savings in areas of the audio stream that cannot be perceived. This is done by, for

instance, using very small numbers of bits to code the high frequencies of most signals - not

because the signal has little high frequency information (though this is also often true as well),

but rather because the human ear can only perceive very loud signals in this region, so that softer

sounds 'hidden' there simply aren't heard.

If reducing perceptual redundancy does not achieve sufficient compression for a particular

application, it may require further lossy compression. Depending on the audio source, this still

may not produce perceptible differences. Speech for example can be compressed far more than

music. Most lossy compression schemes allow compression parameters to be adjusted to achieve

a target rate of data, usually expressed as a bit rate. Again, the data reduction will be guided by

some model of how important the sound is as perceived by the human ear, with the goal of

efficiency and optimized quality for the target data rate. (There are many different models used

for this perceptual analysis, some better suited to different types of audio than others.) Hence,

depending on the bandwidth and storage requirements, the use of lossy compression may result

in a perceived reduction of the audio quality that ranges from none to severe, but generally an

obviously audible reduction in quality is unacceptable to listeners.

Because data is removed during lossy compression and cannot be recovered by decompression,

some people may not prefer lossy compression for archival storage. Hence, as noted, even those

who use lossy compression (for portable audio applications, for example) may wish to keep a

losslessly compressed archive for other applications. In addition, the technology of compression

continues to advance, and achieving a state-of-the-art lossy compression would require one to

begin again with the lossless, original audio data and compress with the new lossy codec. The

nature of lossy compression (for both audio and images) results in increasing degradation of

quality if data are decompressed, then recompressed using lossy compression.

Coding methods

Transform domain methodsIn order to determine what information in an audio signal is perceptually irrelevant, most lossy

compression algorithms use transforms such as the modified discrete cosine transform (MDCT)

to convert time domain sampled waveforms into a transform domain. Once transformed,

typically into thefrequency domain, component frequencies can be allocated bits according to

how audible they are. Audibility of spectral components is determined by first calculating

a masking threshold, below which it is estimated that sounds will be beyond the limits of human

perception.

The masking threshold is calculated using the absolute threshold of hearing and the principles

ofsimultaneous masking - the phenomenon wherein a signal is masked by another signal

separated by frequency - and, in some cases, temporal masking - where a signal is masked by

another signal separated by time. Equal-loudness contours may also be used to weight the

perceptual importance of different components. Models of the human ear-brain combination

incorporating such effects are often called psychoacoustic models.

Time domain methodsOther types of lossy compressors, such as the linear predictive coding (LPC) used with speech,

aresource-based coders. These coders use a model of the sound's generator (such as the human

vocal tract with LPC) to whiten the audio signal (i.e., flatten its spectrum) prior to quantization.

LPC may also be thought of as a basic perceptual coding technique; reconstruction of an audio

signal using a linear predictor shapes the coder's quantization noise into the spectrum of the

target signal, partially masking it.

ApplicationsDue to the nature of lossy algorithms, audio quality suffers when a file is decompressed and

recompressed (digital generation loss). This makes lossy compression unsuitable for storing the

intermediate results in professional audio engineering applications, such as sound editing and

multitrack recording. However, they are very popular with end users (particularly MP3), as a

megabyte can store about a minute's worth of music at adequate quality.

UsabilityUsability of lossy audio codecs is determined by:

Perceived audio quality

Compression factor

Speed of compression and decompression

Inherent latency of algorithm (critical for real-time streaming applications; see below)

Product support

Lossy formats are often used for the distribution of streaming audio, or interactive applications

(such as the coding of speech for digital transmission in cell phone networks). In such

applications, the data must be decompressed as the data flows, rather than after the entire data

stream has been transmitted. Not all audio codecs can be used for streaming applications, and for

such applications a codec designed to stream data effectively will usually be chosen.

Latency results from the methods used to encode and decode the data. Some codecs will analyze

a longer segment of the data to optimize efficiency, and then code it in a manner that requires a

larger segment of data at one time in order to decode. (Often codecs create segments called a

"frame" to create discrete data segments for encoding and decoding.) The inherent latency of the

coding algorithm can be critical; for example, when there is two-way transmission of data, such

as with a telephone conversation, significant delays may seriously degrade the perceived quality.

In contrast to the speed of compression, which is proportional to the number of operations

required by the algorithm, here latency refers to the number of samples which must be analysed

before a block of audio is processed. In the minimum case, latency is 0 zero samples (e.g., if the

coder/decoder simply reduces the number of bits used to quantize the signal). Time domain

algorithms such as LPC also often have low latencies, hence their popularity in speech coding for

telephony. In algorithms such as MP3, however, a large number of samples have to be analyzed

in order to implement a psychoacoustic model in the frequency domain, and latency is on the

order of 23 ms (46 ms for two-way communication).

Speech encodingSpeech encoding is an important category of audio data compression. The perceptual models

used to estimate what a human ear can hear are generally somewhat different from those used for

music. The range of frequencies needed to convey the sounds of a human voice are normally far

narrower than that needed for music, and the sound is normally less complex. As a result, speech

can be encoded at high quality using relatively low bit rates.

This is accomplished, in general, by some combination of two approaches:

Only encoding sounds that could be made by a single human voice.

Throwing away more of the data in the signal—keeping just enough to reconstruct an

"intelligible" voice rather than the full frequency range of human hearing.

Perhaps the earliest algorithms used in speech encoding (and audio data compression in general)

were the A-law algorithm and the µ-law algorithm.

History

Solidyne 922: The world's first commercial audio bit compression card for PC, 1990

A literature compendium for a large variety of audio coding systems was published in the IEEE

Journal on Selected Areas in Communications (JSAC), February 1988. While there were some

papers from before that time, this collection documented an entire variety of finished, working

audio coders, nearly all of them using perceptual (i.e. masking) techniques and some kind of

frequency analysis and back-end noiseless coding.[1] Several of these papers remarked on the

difficulty of obtaining good, clean digital audio for research purposes. Most, if not all, of the

authors in the JSAC edition were also active in the MPEG-1 Audio committee.

The world's first commercial broadcast automation audio compression system was developed by

Oscar Bonello, an Engineering professor at the University of Buenos Aires.[2] In 1983, using the

psychoacoustic principle of the masking of critical bands first published in 1967,[3] he started

developing a practical application based on the recently developed IBM PC computer, and the

broadcast automation system was launched in 1987 under the name Audicom. 20 years later,

almost all the radio stations in the world were using similar technology, manufactured by a

number of companies.

Video compressionVideo compression refers to reducing the quantity of data used to represent digital

video images, and is a combination of spatial image compression and temporal motion

compensation. Video compression is an example of the concept of source coding in Information

theory. This article deals with its applications: compressed video can effectively reduce

the bandwidth required to transmit video viaterrestrial broadcast, via cable TV, or via satellite

TV services.

Most video compression is lossy — it operates on the premise that much of the data present

before compression is not necessary for achieving good perceptual quality. For

example, DVDs use a video coding standard called MPEG-2 that can compress around two hours

of video data by 15 to 30 times, while still producing a picture quality that is generally

considered high-quality for standard-definitionvideo. Video compression is

a tradeoff between disk space, video quality, and the cost of hardwarerequired to decompress the

video in a reasonable time. However, if the video is overcompressed in a lossy manner, visible

(and sometimes distracting) artifacts can appear.

Video compression typically operates on square-shaped groups of neighboring pixels, often

calledmacroblocks. These pixel groups or blocks of pixels are compared from one frame to the

next and thevideo compression codec (encode/decode scheme) sends only the differences within

those blocks. This works extremely well if the video has no motion. A still frame of text, for

example, can be repeated with very little transmitted data. In areas of video with more motion,

more pixels change from one frame to the next. When more pixels change, the video

compression scheme must send more data to keep up with the larger number of pixels that are

changing. If the video content includes an explosion, flames, a flock of thousands of birds, or any

other image with a great deal of high-frequency detail, the quality will decrease, or the variable

bitrate must be increased to render this added information with the same level of detail.

The programming provider has control over the amount of video compression applied to their

video programming before it is sent to their distribution system. DVDs, Blu-ray discs, and HD

DVDs have video compression applied during their mastering process, though Blu-ray and HD

DVD have enough disc capacity that most compression applied in these formats is light, when

compared to such examples as most video streamed on the internet, or taken on a cellphone.

Software used for storing video on hard drives or various optical disc formats will often have a

lower image quality, although not in all cases. High-bitrate video codecs with little or no

compression exist for video post-production work, but create very large files and are thus almost

never used for the distribution of finished videos. Once excessive lossy video compression

compromises image quality, it is impossible to restore the image to its original quality.

Video is basically a three-dimensional array of color pixels. Two dimensions serve as spatial

(horizontal and vertical) directions of the moving pictures, and one dimension represents the time

domain. A data frame is a set of all pixels that correspond to a single time moment. Basically, a

frame is the same as astill picture.

Video data contains spatial and temporal redundancy. Similarities can thus be encoded by merely

registering differences within a frame (spatial), and/or between frames (temporal). Spatial

encoding is performed by taking advantage of the fact that the human eye is unable to distinguish

small differences in color as easily as it can perceive changes in brightness, so that very similar

areas of color can be "averaged out" in a similar way to jpeg images (JPEG image compression

FAQ, part 1/2). With temporal compression only the changes from one frame to the next are

encoded as often a large number of the pixels will be the same on a series of frames.

Intraframe versus interframe compressionOne of the most powerful techniques for compressing video is interframe compression.

Interframe compression uses one or more earlier or later frames in a sequence to compress the

current frame, while intraframe compression uses only the current frame, which is effectively

image compression.

The most commonly used method works by comparing each frame in the video with the previous

one. If the frame contains areas where nothing has moved, the system simply issues a short

command that copies that part of the previous frame, bit-for-bit, into the next one. If sections of

the frame move in a simple manner, the compressor emits a (slightly longer) command that tells

the decompresser to shift, rotate, lighten, or darken the copy — a longer command, but still much

shorter than intraframe compression. Interframe compression works well for programs that will

simply be played back by the viewer, but can cause problems if the video sequence needs to be

edited.

Since interframe compression copies data from one frame to another, if the original frame is

simply cut out (or lost in transmission), the following frames cannot be reconstructed properly.

Some video formats, such as DV, compress each frame independently using intraframe

compression. Making 'cuts' in intraframe-compressed video is almost as easy as editing

uncompressed video — one finds the beginning and ending of each frame, and simply copies bit-

for-bit each frame that one wants to keep, and discards the frames one doesn't want. Another

difference between intraframe and interframe compression is that with intraframe systems, each

frame uses a similar amount of data. In most interframe systems, certain frames (such as "I

frames" in MPEG-2) aren't allowed to copy data from other frames, and so require much more

data than other frames nearby.

It is possible to build a computer-based video editor that spots problems caused when I frames

are edited out while other frames need them. This has allowed newer formats like HDV to be

used for editing. However, this process demands a lot more computing power than editing

intraframe compressed video with the same picture quality.

Current formsToday, nearly all video compression methods in common use (e.g., those in standards approved

by theITU-T or ISO) apply a discrete cosine transform (DCT) for spatial redundancy reduction.

Other methods, such as fractal compression, matching pursuit and the use of a discrete wavelet

transform (DWT) have been the subject of some research, but are typically not used in practical

products (except for the use of wavelet coding as still-image coders without motion

compensation). Interest in fractal compression seems to be waning, due to recent theoretical

analysis showing a comparative lack of effectiveness to such methods.

History of Video Compression Standards

Year Standard Publisher DRM-free Popular Implementations

1984 H.120 ITU-T yes

1990 H.261 ITU-T yes

1993 MPEG-1 ISO, IEC yes Video-CD

1996 MPEG-2 ISO, IEC no DVD Video , Blu-Ray , SVCD

2000 MPEG-4 ISO, IEC no Blu-Ray , iPod Video , HD-DVD

Image compressionImage compression is the application of data compression on digital images. In effect, the

objective is to reduce redundancy of the image data in order to be able to store or transmit data in

an efficient form.

A chart showing the relative quality of various jpg settings and also compares saving a file as a

jpg normally and using a "save for web" technique

Image compression can be lossy or lossless. Lossless compression is sometimes preferred for

medical imaging, technical drawings, icons or comics. This is because lossy compression

methods, especially when used at low bit rates, introduce compression artifacts. Lossless

compression methods may also be preferred for high value content, such as medical imagery or

image scans made for archival purposes. Lossy methods are especially suitable for natural

images such as photos in applications where minor (sometimes imperceptible) loss of fidelity is

acceptable to achieve a substantial reduction in bit rate. The lossy compression that produces

imperceptible differences can be called visually lossless.

Methods for lossless image compression are:

Run-length encoding  – used as default method in PCX and as one of possible

in BMP, TGA, TIFF

DPCM  and Predictive Coding

Entropy encoding

Adaptive dictionary algorithms such as LZW – used in GIF and TIFF

Deflation  – used in PNG, MNG and TIFF

Methods for lossy compression:

Reducing the color space to the most common colors in the image. The selected colors

are specified in the color palette in the header of the compressed image. Each pixel just

references the index of a color in the color palette. This method can be combined

with dithering to avoid posterization.

Chroma subsampling . This takes advantage of the fact that the eye perceives spatial

changes of brightness more sharply than those of color, by averaging or dropping some of the

chrominance information in the image.

Transform coding . This is the most commonly used method. A Fourier-related

transform such as DCTor the wavelet transform are applied, followed

by quantization and entropy coding.

Fractal compression .

The best image quality at a given bit-rate (or compression 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 orembedded bitstreams. Despite its contrary nature, scalability can also 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.

Component progressive: First encode grey; then color.

Region of interest coding. Certain parts of the image are encoded with higher quality than

others. This can be combined with scalability (encode these parts first, others later).

Meta information. Compressed data can contain information about the image which can be used

to categorize, search or browse images. Such information can include color and texture statistics,

smallpreview images and author/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 is often 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 judgement of the viewer is also regarded as an important, perhaps the most

important, measure.

Methods

Image

Cartesian Perceptual Compression : Also known as CPC

DjVu

Fractal compression

HAM , hardware compression of color information used in Amiga computers

ICER , used by the Mars Rovers: related to JPEG 2000 in its use of wavelets

JPEG

JPEG 2000 , JPEG's successor format that uses wavelets, for Lossy or Lossless

compression.

JBIG2

PGF , Progressive Graphics File (lossless or lossy compression)

Wavelet compression

S3TC  texture compression for 3D computer graphics hardware

Video

H.261

H.263

H.264

MNG  (supports JPEG sprites)

Motion JPEG

MPEG-1  Part 2

MPEG-2  Part 2

MPEG-4  Part 2 and Part 10 (AVC)

Ogg  Theora (noted for its lack of patent restrictions)

Dirac

Sorenson video codec

VC-1

Audio

Music

AAC

ADPCM

ATRAC

Dolby AC-3

MP2

MP3

Musepack

Ogg  Vorbis (noted for its lack of patent restrictions)

WMA

Speech

CELP

G.711

G.726

Harmonic and Individual Lines and Noise  (HILN)

AMR  (used by GSM cell carriers, such as T-Mobile)

Speex  (noted for its lack of patent restrictions)

Other dataResearchers have (semi-seriously) performed lossy compression on text by either using a

thesaurus to substitute short words for long ones, or generative text techniques [3], although these

sometimes fall into the related category of lossy data conversion.

Glossary

ABR - Average bitrate

CBR - Constant bitrate

VBR - Variable bitrate

REFERENCES

www.wikipedia.com

www.data-compression.com

Abramson, N. 1963. Information Theory and Coding. McGraw-Hill, New York.

Cappellini, V., Ed. 1985. Data Compression and Error Control Techniques with Applications. Academic Press, London.

Cormack, G. V. 1985. Data Compression on a Database System. Commun. ACM 28, 12 (Dec.), 1336-1342.