Lossless compression of grayscale medical images: effectiveness of traditional and state-of-the-art approaches

Proprietary compression schemes have a cost and risk associated with their support, end of life and interoperability. Standards reduce this cost and risk. The new JPEG-LS process (ISO/IEC 14495-1), and the lossless mode of the proposed JPEG 2000 scheme (ISO/IEC CD15444-1), new standard schemes that may be incorporated into DICOM, are evaluated here. Three thousand, six hundred and seventy-nine (3,679) single frame grayscale images from multiple anatomical regions, modalities and vendors, were tested. For all images combined JPEG-LS and JPEG 2000 performed equally well (3.81), almost as well as CALIC (3.91), a complex predictive scheme used only as a benchmark. Both out-performed existing JPEG (3.04 with optimum predictor choice per image, 2.79 for previous pixel prediction as most commonly used in DICOM). Text dictionary schemes performed poorly (gzip 2.38), as did image dictionary schemes without statistical modeling (PNG 2.76). Proprietary transform based schemes did not perform as well as JPEG-LS or JPEG 2000 (S+P Arithmetic 3.4, CREW 3.56). Stratified by modality, JPEG-LS compressed CT images (4.00), MR (3.59), NM (5.98), US (3.4), IO (2.66), CR (3.64), DX (2.43), and MG (2.62). CALIC always achieved the highest compression except for one modality for which JPEG-LS did better (MG digital vendor A JPEG-LS 4.02, CALIC 4.01). JPEG-LS outperformed existing JPEG for all modalities. The use of standard schemes can achieve state of the art performance, regardless of modality, JPEG-LS is simple, easy to implement, consumes less memory, and is faster than JPEG 2000, though JPEG 2000 will offer lossy and progressive transmission. It is recommended that DICOM add transfer syntaxes for both JPEG-LS and JPEG 2000.

[1]  Peter Deutsch,et al.  ZLIB Compressed Data Format Specification version 3.3 , 1996, RFC.

[2]  J Kivijärvi,et al.  A Comparison of Lossless Compression Methods for Medical Images , 2022 .

[3]  S. Golomb Run-length encodings. , 1966 .

[4]  Guillermo Sapiro,et al.  LOCO-I: a low complexity, context-based, lossless image compression algorithm , 1996, Proceedings of Data Compression Conference - DCC '96.

[5]  K. Denecker,et al.  An experimental comparison of several lossless image coders for medical images , 1997, Proceedings DCC '97. Data Compression Conference.

[6]  William A. Pearlman,et al.  Reversible image compression via multiresolution representation and predictive coding , 1993, Other Conferences.

[7]  Susan S. Young,et al.  Statisically lossless image compression for CR and DR , 1999, Medical Imaging.

[8]  Savitri Bevinakoppa,et al.  DIGITAL IMAGE COMPRESSION TECHNIQUES , 2014 .

[9]  Michael W. Marcellin,et al.  Efficient lossless coding of medical image volumes using reversible integer wavelet transforms , 1998, Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225).

[10]  Robert F. Rice,et al.  Some practical universal noiseless coding techniques , 1979 .

[11]  Ahmad Zandi,et al.  CREW: Compression with Reversible Embedded Wavelets , 1995, Proceedings DCC '95 Data Compression Conference.

[12]  Paul Dan Cristea,et al.  Wavelet-based lossless compression of coronary angiographic images , 1999, IEEE Transactions on Medical Imaging.

[13]  Nasir D. Memon,et al.  Context-based, adaptive, lossless image coding , 1997, IEEE Trans. Commun..

[14]  Thomas Boutell,et al.  PNG (Portable Network Graphics) Specification Version 1.0 , 1997, RFC.