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A lossy data 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. This type of compression is used frequently on the Internet and especially in streaming media and telephony applications. These methods are typically referred to as codecs in this context. Contrast with lossless data compression. Depending on the design of the format lossy data compression often suffers from generation loss, that is compressing and decompressing multiple times will do more damage to the data than doing it once.
Types of lossy compression
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 vs. Lossless Compression
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. The compression ratio (that is, the size of the compressed file compared to that of the uncompressed file) of lossy video codecs are nearly always far superior to those of the audio and still-image equivalents. Audio can be compressed at 10:1 with no noticeable loss of quality, video can be compressed immensely with little visible quality loss, eg 300:1. Lossily compressed still images are often compressed to 1/10th their original size, as with audio, but the quality loss is more noticeable, especially on closer inspection.
When a user acquires a lossily-compressed file, (for example, to reduce download-time) the retrieved file can be quite different to the original at the bit level while being indistinguishable to the human ear or eye for most practical purposes. Many methods focus on the idiosyncrasies of the human anatomy, taking into account, for example, that the human eye can see only certain frequencies of light. The psychoacoustic model describes how sound can be highly compressed without degrading the perceived quality of the sound. Flaws caused by lossy compression that are noticeable to the human eye or ear are known as compression artifacts.
Example of Lossy Compression
Original Lena Image (12KB size)
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Lena Image, Compressed (85% less information, 1.8KB)
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Lena Image, Highly Compressed (96% less information, 0.56KB)
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The above images show the use of lossy compression to reduce the file size of the image. The image is an excerpt of the image of Lena, a de facto industry-standard test image.
- The first picture is 12,249 bytes.
- The second picture has been compressed (JPEG quality 30) and is 85% smaller, at 1,869 bytes. Notice the loss of detail in the brim of the hat.
- The third picture has been highly compressed (JPEG quality 5) and is 96% smaller, at 559 bytes. The compression artifacts are much more noticeable.
Even though the third image has high distortion, the face is still recognizable. Good lossy compression algorithms are able to throw away "less important" information and still retain the "essential" information.
Lossy compression methods
Still image compression
Moving image compression
Sound compression
Music
Speech
Other types of data
Technically, reducing text size by removing all vowels can be considered a lossy data compression as well. The text is usually still readable from the context given by the consonants. Researchers have also performed lossy compression on text by either using a thesaurus to substitute short words for long ones, or generative text techniques [1], although these sometimes fall into the related category of Lossy data conversion.
See also
External links
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