The OpenEXR File Format


Industrial Light + Magic implemented its own extended dynamic range file format in Summer 2000. The existing 8-bit file format used at the time could not accurately reproduce images with extreme contrast between the darkest and brightest regions, or images with very subtle color gradations.

ILM's extended dynamic range file format has been employed successfully in the movies Harry Potter, Men in Black II, and Signs. Several shows currently in production at ILM are using the new format.

Realizing that various other parties are interested in an extended dynamic range file format, ILM decided to polish its new file format a bit, and to publish it. OpenEXR is the result.

Features of OpenEXR

high dynamic range
Pixel data are stored as 16-bit or 32-bit floating-point numbers. With 16 bits, the representable dynamic range is significantly higher than the range of most image capture devices: 109 or 30 f-stops without loss of precision, and an additional 10 f-stops at the low end with some loss of precision. Most 8-bit file formats have around 7 to 10 stops.

good color resolution
with 16-bit floating-point numbers, color resolution is 1024 steps per f-stop, as opposed to somewhere around 20 to 70 steps per f-stop for most 8-bit file formats. Even after significant processing, for example extensive color correction, images tend to show no noticeable color banding.

compatible with graphics hardware
The 16-bit floating-point data format is fully compatible with the 16-bit frame-buffer data format used in some new graphics hardware. Images can be transferred back and forth between an OpenEXR file and a 16-bit floating-point frame buffer without losing data.

lossless data compression
The data compression methods currently implemented in OpenEXR are lossless; repeatedly compressing and uncompressing an image does not change the image data. With the current compression methods, photographic images with significant amounts of film grain tend to shrink to somewhere between 35 and 55 percent of their uncompressed size. New lossless and lossy compression schemes can be added in the future.

arbitrary image channels
OpenEXR images can contain an arbitrary number and combination of image channels, for example red, green, blue, and alpha, Y, U, and V (luminance, and two sub-sampled chroma channels), depth, surface normal directions, or motion vectors.

ability to store additional data
Often it is necessary to annotate images with additional data; for example, color timing information, process tracking data, or camera position and view direction. OpenEXR allows storing of an arbitrary number of extra "attributes", of arbitrary type, in an image file. Software that reads OpenEXR files ignores attributes it does not understand.

easy-to-use C++ and C programming interfaces
In order to make writing and reading OpenEXR files easy, the file format was designed together with a C++ programming interface. Two levels of access to image files are provided: a fully general interface for writing and reading files with arbitrary sets of image channels, and a specialized interface for the most common case (red, green, blue, and alpha channels, or some subset of those). Additionally, a C-callable version of the programming interface supports reading and writing OpenEXR files from programs written in C.

The OpenEXR file format is hardware and operating system independent. While implementing the C and C++ programming interfaces, an effort was made to use only language features and library functions that comply with the C and C++ ISO standards. The resulting code is known to work on Linux (Intel x86), SGI Irix (Mips), and Mac OSX (PowerPC).

Overview of the OpenEXR File Format


File Structure

An OpenEXR file has two main parts: the header and the pixels.

The header is a list of attributes that describe the pixels. An attribute is a named data item of an arbitrary type. To ensure that OpenEXR files written by one program can be read by other programs, certain required attributes must be present in all OpenEXR file headers:

name description
The image's display and data window.
pixelAspectRatio Width divided by height of a pixel when the image is displayed with the correct aspect ratio. A pixel's width (height) is the distance between the centers of two horizontally (vertically) adjacent pixels on the display.
channels Description of the image channels stored in in the file.
compression Specifies the compression method applied to the pixel data of all channels in the file.
lineOrder Specifies in what order the scan lines in the file are stored in the file (increasing Y or decreasing Y).

Besides the required attributes, a program may may place any number of additional attributes in the file's header. Often it is necessary to annotate images with additional data, for example color timing information, process tracking data, or camera position and view direction. Those data can be packaged as extra attributes in the image file's header.

In an OpenEXR file, the pixels of an image are stored in horizontal rows or scan lines. When an image file is written, the scan lines must be written either in increasing Y order (top scan line first) or in decreasing Y order (bottom scan line first). When an image file is read, random-access to the scan lines is possible; the scan lines can be read in any order. Reading the scan lines in the same order as they were written causes the file to be read sequentially, without "seek" operations, and as fast as possible.

Data Compression

OpenEXR currently offers three different data compression methods, with various speed versus compression ratio tradeoffs. All three compression schemes are lossless; compressing and uncompressing does not alter the pixel data. Optionally, the pixels can be stored in uncompressed form. With fast filesystems, uncompressed files can be written and read significantly faster than compressed files.

Supported compression schemes:
name description
PIZ A wavelet transform is applied to the pixel data, and the result is Huffman-encoded. This scheme tends to provide the best compression ratio for the types of images that are typically processed at Industrial Light + Magic. Files are compressed and decompressed at roughly the same speed. For photographic images with significant amounts of film grain (OpenEXR sample images and frames from ILM's current productions) the files are reduced to between 35 and 55 percent of their uncompressed size.
ZIP Differences between horizontally adjacent pixels are compressed using the open source zlib library. ZIP decompression is faster than PIZ decompression, but ZIP compression is significantly slower. Photographic images tend to shrink to between 45 and 55 percent of their uncompressed size.
RLE Run-length encoding of differences between horizontally adjacent pixels. This method is fast, and works well for images with large flat areas, but for photographic images, compressed file size is usually between 60 and 75 percent of the uncompressed size.

The HALF Data Type

Image channels of type HALF are stored as 16-bit floating-point numbers. The 16-bit floating-point data type is implemented as a C++ class, half, which was designed to behave as much as possible like the standard floating-point data types built into the C++ language. In arithmetic expressions, numbers of type half can be mixed freely with float and double numbers; in most cases, conversions to and from half happen automatically.

half numbers have 1 sign bit, 5 exponent bits, and 10 mantissa bits. The interpretation of the sign, exponent and mantissa is analogous to IEEE-754 floating-point numbers. half supports normalized and denormalized numbers, infinities and NANs (Not A Number). The range of representable numbers is roughly 6.010-8 - 6.5104; numbers smaller than 6.110-5are denormalized. Conversions from float to half round the mantissa to 10 bits; the 13 least significant bits are lost. Conversions from half to float are lossless; all half numbers are exactly representable as float values.

The data type implemented by class half is identical to Nvidia's 16-bit floating-point format ("fp16 / half"). 16-bit data, including infinities and NANs, can be transferred between OpenEXR files and Nvidia 16-bit floating-point frame buffers without losing any bits.

What's in the Numbers?

We store linear values in the RGB 16-bit floats. By this we mean that each value is linear relative to the amount of light it represents. This implies that display of images requires some processing to account for the non-linear response of a typical display. In its simplest form, this is a power function to perform gamma correction. There many recent papers on the subject of tone mapping to represent the high dynamic range of light values on a display. By storing linear data in the file (double the number, double the light), we have the best starting point for these downstream algorithms. Also, most commercial renderers produce linear values (before gamma is applied to output to lower precision formats).

With this linear relationship established, the question remains, What number is white? The convention we employ is to determine a middle gray object, and assign it the photographic 18% gray value, or .18 in the floating point scheme. Other pixel values can be easily determined from there (a stop brighter is .36, another stop is .72). The value 1.0 has no special significance (it is not a clamping limit, as in other formats); it roughly represents light coming from a 100% reflector (slightly brighter than paper white). But there are many brighter pixel values available to represent objects such as fire and highlights. The range of normalized 16-bit floats can represent thirty stops of information with 1024 steps per stop. We have eighteen and a half stops over middle gray, and eleven and a half below. The denormalized numbers provide an additional ten stops with decreasing precision per stop.

Recommendations and Open Issues

RGB Color

Simply calling the R channel red is not sufficient information to determine accurately the color that should be displayed for a given pixel value. We suggest that a set of CIE coordinates be provided as attributes (RedCIE, GreenCIE, BlueCIE, WhiteCIE) which will define the intended colors stored in the OpenEXR file. The attributes are 2D vectors (type v2f) containing the CIE x,y locations for the primaries, plus a white point. Since there is no maximum white value, we suggest storing the color locations for the 18% pixel values. The white point would then be the desired CIE location of (.18, .18, .18). If the primaries and white point for a given display are known, a color transform can correctly be done. The OpenEXR package does not perform this transformation; it is left to the display software. In the absence of specific CIE coordinates, the display software would assume the primaries match the display.

Channel Names

An OpenEXR image can have any number of channels with arbitrary names. The specialized RGBA image interface assumes that channels with the names "R", "G", "B" and "A" mean red, green, blue and alpha. No predefined meaning has been assigned to any other channels. However, for a few channel names we recommend the interpretations given in the table below. We expect this table to grow over time as users employ OpenEXR for data such as shadow maps, motion-vector fields or images with more than three color channels.
name interpretation
Y luminance, used either alone, for gray-scale images, or in combination with U and V for color images.
U, V chroma for YUV images, see below.
AR, AG, AB red, green and blue alpha/opacity, for colored mattes (required to composite images of objects like colored glass correctly).

YUV Images

We haven't done much work with high dynamic-range YUV images. We have no recommendations on how to handle chroma subsampling or how to interpret individual YUV triples. A future version of OpenEXR should probably have a specialized YUV file interface that handles these and other issues. We are open to suggestions and code submissions for this interface.


Mail to: (Note: this is temporary; a public OpenEXR mail list will be created soon.)


The ILM OpenEXR file format was designed and implemented by Florian Kainz and Rod Bogart. The PIZ compression scheme is based on an algorithm by Christian Rouet. Josh Pines helped extend the PIZ algorithm for 16-bit and found optimizations for the float to half conversions. Drew Hess packaged and adapted ILM's internal source code for public release.

Industrial Light + Magic, Marin County, California, August 2002.