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Novel compression method increases storage fourfold


A Japanese lossless compression technique provides greater compression without data loss than other lossless techniques, according to new research.

A Japanese lossless compression technique provides greater compression without data loss than other lossless techniques, according to new research.

Lossless image compression technology provides an environment in which data size can be reduced without losing diagnostic information. Standard lossless compression techniques such as JPEG, JPEG2000, and even wavelet methods, however, can only reduce the image to approximately one-half to one-third original size.

"Our method improves the compression rate of diagnostic images up to four to six times greater than conventional methods in lossless compression mode," said Dr. Hiroshi Fukatsu of the radiology department at Nagoya University Hospital.

Fukatsu's method provides compression ratios up to 15:1, while JPEG or JPEG2000 compression rates are only 2.5:1 (Radiat Med 2008;26:120-128).

This technique could improve the efficiency of handling the increasing volume of medical imaging data.

"Our hospital owns a 30-TB server, but this covers only two years worth of imaging data, even if we apply conventional JPEG or JPEG2000 lossless compression," he said.

With Fukatsu's new method, the same server could store more than nine years of data without adding physical disc space.

The Japanese method supports both gray-scale and color images, can compress dynamic images, and places a low load on processing units, Fukatsu said.

Fukatsu's team achieved this with a unique lossless image compression technique employing a artificial intelligence-like neural network. Encoding consists of:

  • a prediction block

  • a residual data calculation block

  • a transformation and quantization block

  • an organization and modification block

  • an entropy encoding block

The predicted image is divided into four macroblocks using the original image for training and then redivided into 16 subblocks, Fukatsu said. The predicted image is compared with the original image to create the residual image. The spatial and frequency data of the residual image are then compared and transformed.

Fukatsu used special software that compares the values of each pixel of the images before and after compression and highlights pixels whose values had changed.

"The data loss of the images compressed using our method was found to be zero," Fukatsu said.

This comparison method can serve as a tool to determine whether lossless compression is truly lossless.

Fukatsu cautions that only plain chest radiographs were used in the experiments and that compression rates for other regions, such as abdomen and head, were not evaluated.

"Also, for CT and MR images, not all regions of the body were evaluated, and the influence of the differences in scan conditions were not considered," he said.

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