Compression research expands from wavelets to Glicbawls

It's tough satisfying the demand to stream real-time images over existing communication infrastructures to almost anywhere in the hospital. Compression technology research, whether from corporate laboratories or solitary efforts by doctoral candidates, is itself being squeezed.

From corporate labs come technologies such as GE's TruRez, a fast-wavelet compression scheme and the topic of an infoRAD exhibit at the RSNA meeting in December.

The exhibit provided a point-by-point comparison of JPEG2000 to TruRez, showing how TruRez can achieve higher speed while preserving most features and sacrificing little in terms of compression ratio. JPEG2000 has many useful features, but the principal disadvantage over its predecessor JPEG is increased decoding time.

"JPEG2000 is much slower than JPEG," said Sudipta Mukhopadhyay, Ph.D., a GE Medical Systems scientist at the John F. Welch Technology Centre in Bangalore, India. "This delay tends to reduce radiologist productivity."

Two major computational burdens for JPEG2000 are the Adaptive Arithmetic coder and bit plane coding, Mukhopadhyay said. TruRez uses wavelet transform and Huffman code instead.

"While Adaptive Arithmetic code is superior to standard Huffman table in compression performance, the intelligent context-sensitive Huffman table design used in TruRez reduces this performance gap," he said.

Enterprise distribution of images has motivated a number of vendors to include image compression technologies into their PACS. Agfa has Impax Web 1000, GE has TruRez, RealTimeImage has iPACS, and Stentor has iSyntax.

Another compression technique is the product of a single researcher. While its relevance to compressing radiologic images is as yet unknown, Glicbawls (for grey level image compression by adaptive weighted least squares) is a lossless and/or near-lossless image compression algorithm written by Bernd Meyer, a Ph.D. candidate and part-time research fellow at the School of Computer Science and Software Engineering at Monash University in Melbourne, Australia.

Computer scientists claim Glicbawls does well on gray-scale images.

"Glicbawls achieves lossless performance on gray-scale still images that is superior to standard algorithms like (lossless image compressors) CALIC and LOCO," said Charles Creusere, Ph.D., an assistant professor of electrical and computer engineering at New Mexico State University. "Because the source code is so compact (only 1795 bytes) it achieves slightly superior performance even when sent with the decoder to create a self-extracting file."

The downside is that it is somewhat slow. Glicbawls can take close to a minute to encode or decode an image. The source code is available at http://byron.csse.monash.edu.au/glicbawls/.