# Handling of compressed pixel data

How to get compressed pixel data

## Preconditions

To be able to decompress compressed DICOM pixel data, you need to install one or more packages that are able to handle the format the data is encoded in.

The following packages can be used with pydicom:

- GDCM - this is the package that supports most compressed formats

- Pillow, ideally with `jpeg` and `jpeg2000` plugins

- jpeg_ls

- pylibjpeg, with the `-libjpeg`, `-openjpeg` and `-rle` plugins

Note that you always need the NumPy package to be able to handle pixel data.

Caution

We rely on the data handling capacity of the mentioned packages and cannot guarantee the correctness of the generated uncompressed pixel data. Be sure to verify the correctness of the output using other means before you use them for medical purposes.

## Supported Transfer Syntaxes

To get the transfer syntax of a dataset you can do:

    >>> from pydicom import dcmread
    >>> ds = dcmread('path/to/dicom/file')
    >>> ds.file_meta.TransferSyntaxUID
    '1.2.840.10008.1.2.1'
    >>> ds.BitsAllocated
    16

As far as we have been able to verify, the following transfer syntaxes are handled by the given packages:

Transfer Syntax | NumPy | NumPy + JPEG-LS | NumPy + GDCM | NumPy + Pillow | NumPy + pylibjpeg
--- | --- | --- | --- | --- | ---
Name | UID
Explicit VR Little Endian | 1.2.840.10008.1.2.1 | ✓ | ✓ | ✓ | ✓ | ✓
Implicit VR Little Endian | 1.2.840.10008.1.2 | ✓ | ✓ | ✓ | ✓ | ✓
Explicit VR Big Endian | 1.2.840.10008.1.2.2 | ✓ | ✓ | ✓ | ✓ | ✓
Deflated Explicit VR Little Endian | 1.2.840.10008.1.2.1.99 | ✓ | ✓ | ✓ | ✓ | ✓
RLE Lossless | 1.2.840.10008.1.2.5 | ✓ | ✓ | ✓ | ✓ | ✓4
JPEG Baseline (Process 1) | 1.2.840.10008.1.2.4.50 |  |  | ✓ | ✓1 | ✓5
JPEG Extended (Process 2 and 4) | 1.2.840.10008.1.2.4.51 |  |  | ✓ | ✓1,3 | ✓5
JPEG Lossless (Process 14) | 1.2.840.10008.1.2.4.57 |  |  | ✓ |  | ✓5
JPEG Lossless (Process 14, SV1) | 1.2.840.10008.1.2.4.70 |  |  | ✓ |  | ✓5
JPEG LS Lossless | 1.2.840.10008.1.2.4.80 |  | ✓ | ✓ |  | ✓5
JPEG LS Lossy | 1.2.840.10008.1.2.4.81 |  | ✓ | ✓ |  | ✓5
JPEG2000 Lossless | 1.2.840.10008.1.2.4.90 |  |  | ✓ | ✓2 | ✓6
JPEG2000 | 1.2.840.10008.1.2.4.91 |  |  | ✓ | ✓2 | ✓6
JPEG2000 Multi-component Lossless | 1.2.840.10008.1.2.4.92 |  |  |  |  |
JPEG2000 Multi-component | 1.2.840.10008.1.2.4.93 |  |  |  |  |

1 only with JpegImagePlugin
2 only with Jpeg2KImagePlugin
3 only if (0028,0100) Bits Allocated = 8
4 with the pylibjpeg-rle plugin and using the `decompress()` method, 4-5x faster than default
5 with the pylibjpeg-libjpeg plugin
6 with the pylibjpeg-openjpeg plugin

## Usage

To get uncompressed pixel data you have two options:

- use `decompress()` on the dataset to convert it in-place and work with the pixel data as described before

- get an uncompressed copy of the pixel data as a NumPy `ndarray` using `Dataset.pixel_array` without changing the original dataset

Note

Using `decompress()` adapts the transfer syntax of the data set, but not the Photometric Interpretation. The Photometric Interpretation may not match the pixel data, depending on the used decompression handler.