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Multiple methods of data compression are known. For example, published PCT application WO99/46186 describes a method of image compression in which groups of pixels in an image are predicted and then quantised so that the data rate necessary for storing the image is minimised. On the other hand, the technique of transform coding is widely used in many areas, such as video data compression, for example, as described in I. S. Reed and T. Bell, “Techniques for the conversion of the truncated discrete cosine transform”, IEEE Trans. on Communications, vol. COM-26, no. 12, pp. 2249-2261, 1978. Transform coding relies on the fact that most natural images are sparse in some transform domain, so that the data can be represented as a few (usually quite small) coefficients.
In transform coding, the image is transformed into a transform domain, typically the Discrete Cosine Transform (DCT), and quantised. The transform domain coefficients are then entropy coded and the resulting bits are written to storage as an image. Transform coding can produce high quality images at a very low data rate. However, the compression achieved using transform coding is relatively low since the signal is mapped to a frequency domain where it is highly correlated and all the energy is concentrated in only a few coefficients.
More recently, there has been much work in coding images directly in the spatial domain, for example, as described in Y. A. Orkina and G. J. Sullivan, “A block processing scheme for coding of grey level images”, IEEE Trans. on Acoustics, Speech, and Signal Processing, vol. 37, no. 5, pp. 644-649, 1989. The compression is achieved by applying a low-pass filter to the original image and quantising the resulting image. By using the absolute value of the image, the resultant image is sparse. In other words, it has relatively few
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