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compression.md
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@ -55,7 +55,7 @@ The following is an overview of some most common compression techniques.
A famous family of dictionary compression algorithms are **Lempel-Ziv (LZ)** algorithms -- these two guys first proposed [LZ77](lz77.md) in (1977, sliding window) and [LZ78](lz78.md) (explicitly stored dictionary, 1978). These were a basis for improved/remix algorithms, most notably [LZW](lzw.md) (1984, Welch). Additionally these algorithms are used and combined in other algorithms, most notably [gif](gif.md) and [DEFLATE](deflate.md) (used e.g. in gzip and png).
An approach similar to the predictor may be trying to find some general mathematical [model](model.md) of the data and then just find and store the right parameters of the model. This may for example mean [vectorizing](vector_graphics.md) a bitmap image, i.e. finding geometrical shapes in an image composed of pixels and then only storing the parameters of the shapes -- of course this may not be 100% accurate, but again if we want to preserve the data accurately, we may additionally also store the small amount of errors the model makes. Similar approach is used in [vocoders](vocoder.md) used in cellphones that try to mathematically model human speech (however here the compression is lossy), or in [fractal](fractal.md) compression of images. A nice feature we gain here is the ability to actually "increase the resolution" (or rather generate detail) of the original data -- once we fit a model onto our data, we may use it to tell us values that are not actually present in the data (i.e. we get a fancy [interpolation](interpotation.md)/[extrapolation](extrapolation.md)).
An approach similar to the predictor may be trying to find some general mathematical [model](model.md) of the data and then just find and store the right parameters of the model. This may for example mean [vectorizing](vector_graphics.md) a bitmap image, i.e. finding geometrical shapes in an image composed of pixels and then only storing the parameters of the shapes -- of course this may not be 100% accurate, but again if we want to preserve the data accurately, we may additionally also store the small amount of errors the model makes. Similar approach is used in [vocoders](vocoder.md) used in cellphones that try to mathematically model human speech (however here the compression is lossy), or in [fractal](fractal.md) compression of images. A nice feature we gain here is the ability to actually "increase the resolution" (or rather generate detail) of the original data -- once we fit a model onto our data, we may use it to tell us values that are not actually present in the data (i.e. we get a fancy [interpolation](interpolation.md)/[extrapolation](extrapolation.md)).
Another property of data to exploit may be its sparsity -- if for example we have a huge image that's prevalently white, we may say white is the implicit color and we only somehow store the pixels of other colors.
@ -230,4 +230,4 @@ How well does this perform? If we try to let the utility compress its own source
## See Also
- [procedural generation](procgen.md)
- [minification](minification.md)
- [minification](minification.md)