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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 a particular purpose. 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.
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 – for example, in principle, if one starts with an analog or high-resolution digital master, an MP3 file of a given bitrate (e.g. 320 kbit/s) should provide a better representation than a raw uncompressed audio in WAV or AIFF file of the same bitrate. (Uncompressed audio can get lower bitrate only by lowering sampling frequency and/or sampling resolution.) 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 backward compatibility and graceful degradation: in color television, encoding color via a luminance-chrominance transform domain (such as YUV) means that black-and-white sets display the luminance, while ignoring the color information.
Another example is chroma subsampling: the use of color spaces such as YIQ, used in NTSC, allow one to reduce the resolution on the components to accord with human perception – humans have highest resolution for black-and-white (luma), lower resolution for mid-spectrum colors like yellow and green, and lowest for red and blues – thus NTSC displays approximately 350 pixels of luma per scanline, 150 pixels of yellow vs. green, and 50 pixels of blue vs. red, which are proportional to human sensitivity to each component.
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.
* 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 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.
* Video can be compressed immensely (e.g. 100:1) with little visible quality loss;
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.
jpegtran, and the derived exiftran (which also preserves Exif information), and Jpegcrop (which provides a Windows interface).
These allow the image to be
JPEGjoin allows different JPEG images which have the same encoding to be joined without re-encoding. (See also: New jpegtran features.)
Some changes can be made to the compression without re-encoding:
The freeware Windows-only IrfanView has some lossless JPEG operations in its JPG_TRANSFORM plugin.
split and cat.
Fission by Rogue Amoeba on the Macintosh platform will also allow you to join and split MP3 and m4a (Advanced Audio Coding) files without incurring an additional generational loss.
;Gain: Various Replay Gain programs such as MP3gain allow the gain (overall volume) of MP3 files to be modified losslessly.
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. 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.
Without this capacity, which is often the case in practice, to produce a representation with lower resolution or lower fidelity than a given one, one needs to start with the original source signal and encode, or start with a compressed representation and then decompress and re-encode it (transcoding), though the latter tends to cause digital generation loss.
Some audio formats feature a combination of a lossy format and a lossless correction which when combined reproduce the original signal; the correction can be stripped, leaving a smaller, lossily compressed, file. Such formats include MPEG-4 SLS (Scalable to Lossless), WavPack, and OptimFROG DualStream.
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 will transmit thumnails of its encounter with Pluto-Charon before it sends the higher resolution images.
This text is licensed under the Creative Commons CC-BY-SA License. This text was originally published on Wikipedia and was developed by the Wikipedia community.
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