This is a report that introduces the technical features of the different image compression schemes found in the DICOM standar for medical imaging archiving and communication.

1. Medical Images Compression: JPEG variations for DICOM standard
Author: Jose Pablo Pinilla Gomez
The DICOM (Digital Imaging and Communication in Medicine) is a standard managed by the Medical
Imaging & Technology Alliance to ensure the interoperability of medical systems, images, documents
and workflow. It was released in 1993 (updated in 2011), to solve the problem with CT (Computed
Tomography) or MRI (Magnetic Resonance Imaging) images which cannot be decoded by any means
other than those provided by the same manufacturers. This discordance between devices causes
unfair competition among medical machines manufacturers by keeping health care facilities from
acquiring alternate machines that can lead to device incompatibility. Although there are data
“translators” to interconnect different standard systems, their implementation increases the total cost
and delay of the system. A standardized system is then necessary for the implementation of a PACS
(Picture Archiving and Communication System) that compiles all kinds of acquirable images for
medical records. The image file format specified in the current DICOM standard supports RLE (Run
Length Encoding), JPEG, JPEG-LS and JPEG-2000 compression and decompression schemes.
RLE is a lossless compression scheme that converts repeating byte sequences into two-byte codes
with the value of the repeating byte and the negative representation of the number of bytes in the run,
this is called a Replica Run; In case a sequence of non-repetitive bytes is found, the resulting code will
have one byte with the positive representation of the number of bytes in the sequence followed by the
literal sequence (Literal Run)1. RLE can yield good compression ratios for monochrome images but it
has a very variable behaviour for RGB and YCbCr images. The use of RLE is often preceded by other
algorithms to improve its performance.
The Joint Photographic Experts Group created the JPEG standard in 1992. It relies on a five step
process to compress pixel data with a configurable parameter of quality percentage. The processing
begins by changing the color space from the pixel RGB (Red, Green and Blue) triplet to the YCbCr
(Lluminance,Red-Chrominance and Blue-Chrominance) values. As many other image compression
schemes, JPEG uses YCbCr color space to keep the lluminance values that are more relevant to the
human eye while discarding some of the chrominance information. After decompression, files with
“chroma subsampling” are still able to display images with almost unnoticeable changes. A
configurable amount of chrominance (hue and saturation) information is discarded by a subsequent
downsampling step in JPEG.
After subsampling, the next step is to divide the whole image into 8x8 matrices of the same channel
(Y, Cb and Cr). Then, every 8x8 pixel block goes through a Discrete Cosine Transform (DCT)
computation of coefficients. The DCT gives back 64 values (coefficients) that are related to the
“concentration” of one of 64 8x8 blocks with different patterns that can represent the original image
when put together, or give a close approximation of it. Each of the predefined patterns is called a
“basis function” and it is a two-dimensional set of curves created using the cosine function.
The resulting 8x8 matrix with the coefficients is now run through a step of “Quantization”, where every
value in the input matrix is divided by a corresponding constant derived from predefined quantization
tables and user configuration of quality; the resulting values are rounded to the nearest integers. As
stated in the JPEG standard2 “The purpose of quantization is to achieve further compression by
representing DCT coefficients with no greater precision than is necessary to achieve the desired
1 DICOM standard Part 5: Data Structures and Encoding. 2011. Annex G. P101.
http://medical.nema.org/Dicom/2011/11_05pu.pdf
2 JPEG Still Picture Compression Standard. 1991. p5. http://white.stanford.edu/~brian/psy221/reader/Wallace.JPEG.pdf

2. image quality. Stated another way, the goal of this processing step is to discard information which is
not visually significant.” The outcome of quantizing the DCT coefficients is that smaller, unimportant
coefficients will disappear and larger coefficients will lose unnecessary precision.
The final step in JPEG compression is called “Coding” which involves three sub-processes: DC
Coding, Zig-Zag Sequence and Entropy Coding. The “DC” coefficient in the matrix, which is the top
left-most value, contains a higher integer that is a measure of the average value of the 64 image
samples. DC coding replaces every block's DC coefficient with the difference between it and the one
of the previous block, this values are often correlated and therefore produce a small difference. Zig-
Zag Sequence is a literal zig-zag change in the organization of the matrix, taking advantage of the
differentiation between “high-frequency” and “low-frequency” coefficients; the result of this process is
that non-zero (low-frequency) and zero (high-frequency) values are grouped so that the following
compression steps are more effective. The entropy coding uses the Huffman method which can
replace repeating zeroes at the end of a matrix with a single character (byte) and use prefix coding * for
other characters according to their frequency of appearance, more frequent characters are coded into
shorter values.
JPEG's algorithm is very fast, with an average decompression time of 0.9 seconds 3, but it is overall a
lossy standard and it reflects it in most of its steps. Color space transformation is an irreversible
process because computer calculations of decimal point numbers are always approximations of the
real values. Subsampling is a straightforward elimination of data but this step is optional, unlike block
splitting in which certain blocks may require “filling” algorithms to complete the 8x8 matrices and
thereby altering the decompressed result. Next is the DCT which isn't by itself a lossy process, but
forward quantization always is. Quantization is a many-to-one mapping, and therefore is
fundamentally lossy. It is the principal source of lossiness in DCT-based encoders.4
Medical Imaging applications often require high precision so that digital alterations can't affect a
physician's criteria based on radiology results. This doesn't mean that the compression schemes in
Medical PACS have to be lossless, but they need an appropriate level of fidelity. On the other hand,
fidelity is a very subjective qualification which is why DICOM also introduced the lossless JPEG
scheme as well as the lossless/near-lossless JPEG-LS and the JPEG 2000 later on, as alternatives to
the “baseline” JPEG standard5.
Lossless JPEG standard was added as a JPEG mode of operation in 1993. It replaces the DCT and
other lossy steps (Quantization, Chroma Subsampling) from the original format with a new coding
called Differential Pulse Code Modulation (DCPM). DCPM is based on the prediction of an image
block based on its surroundings, the values in these predictions are generally accurate which is why
the difference between them and the ones achieved in the compression tends to be small. Thereby,
this variation of the standard saves these differences and them compresses them even further using
the Huffman method. The downside of lossless JPEG is that it cannot achieve compressions ratios
comparable to those of baseline JPEG compression.
* Prefix property states that there is no valid code word in the system that is a prefix (start) of any other valid code word
in the set.
3 JPEG 2000 still image coding versus other standards. 2000. http://www.jpeg.org/public/wg1n1816.pdf
4 SCHROEDER, Mark D.. JPEG Compression Algorithm and Associated Data Structures. 1997.
http://akbar.marlboro.edu/~mahoney/courses/Fall01/computation/compression/jpeg/jpeg.html
5 The JPEG Committee. Medical Imaging. 2007. http://www.jpeg.org/apps/medical.html

3. In 1999 the Joint Photographic Experts Group released the JPEG-LS standard as a solution to
achieve better lossless compression. It uses the prediction, RLE, and context modelling (surrounding
blocks dependency) coding techniques included in the LOCO-I algorithm created by Hewlett Packard
(HP). This scheme helps to achieve a better compression while keeping the integrity of information in
accord to user's configuration6. Therefore, this standard is classified as lossless/near-lossless, where
near-lossless refers to a change from 1 to 5 between the original RGB values and the uncompressed
versions7. It takes approximately the same time to decompress a JPEG-LS file and a lossless JPEG
but the compression ratio achieved is better.
The latest standard included in the DICOM compatibility was JPEG 2000 which in addition to having a
lossless mode, it includes a set of features that are advantageous in Medical Imaging applications,
such as: Regions of Interest, scalability(Low-resolution previewing) and extensive MetaData. The
differences between baseline JPEG and JPEG 2000 algorithms reflect a new approach in which the
objective of higher compression is always accompanied by a lossless alternative. That is, the Color
Space Transformation can either be irreversible or reversible by removing the quantization errors by
using integers; instead of block splitting, JPEG 2000 uses variable size “tiles” to remove undesired
block filling; the DCT is replaced by a Wavelet Transform (WT) with lossy and lossless variations,
again by removing quantization; and it also runs a coding algorithm afterwards. The WT differs from
the DCT, in that, instead of cosine functions it uses another kind of oscillating functions called
wavelets. A wavelet's amplitude starts from zero, reaches its maximum value and then fades back to
zero creating a “frequency pulse”. The effect of this change in the compression algorithm is that this
transform's coefficients are centred around zero (can be easily coded), with a very few large
coefficients. Nevertheless, JPEG 2000's algorithms make image compression and decompression
relatively slow, taking an average of 4.3s to decompress an image.
Although there is a limitation to JPEG file formats in the DICOM standard, there is a high range of
possibilities that rise from the different JPEG versions and the quality configuration capabilities; in
addition, JPEG is now working in collaboration with DICOM to further improve this integration so that
Medical Imaging necessities are taken under consideration8. But still, when it comes to image quality
there is a lot of subjectivity in order to define how lossy an image can be, taking into account that
lossless algorithms cannot yield as good compression results as those with lossy schemes. Therefore,
the decision to implement a lossy or lossless scheme, and how lossy it can be is now turning into a
decision of what features and performance is required by the application.
Lossless and near-lossless standards with good speed, compression ratio, and new features have the
Medical Imaging industry as one of its targets. However, in order to improve any of those features the
system has to sacrifice performance in another, for example, the average compression ratio for the
lossless configurations of JPEG(2.09), JPEG-LS(2.98) and JPEG 2000(2.50) differ, making JPEG-LS
the best choice for lossless compression, also due to it's fast performance. But if a certain amount of
quality can be compromised (near-lossless), the JPEG algorithm will give better results in considerably
less time. Although, when compared to JPEG 2000 that offers the “Medical-Imaging-oriented”
features, JPEG 2000 will outperform JPEG by giving out the same compression ratios with more
quality settings, taking into account that this superiority is toned down by the amount of time that it
takes to decompress.
6 The JPEG Committee. Lossless JPEG. JPEG-LS. 2007. http://www.jpeg.org/jpeg/jpegls.html
7 Lossless, Near-lossless and Lossy Compression of PTM Images. HP. 2001.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.22.1635&rep=rep1&type=pdf
8 The JPEG Committee. Medical Imaging. 2007. http://www.jpeg.org/apps/medical.html