A New(-ish) Approach

Even with all of the above techniques, plus a lot of effort, the results may not have maximum impact. Perhaps you, like me, have wondered if there is another way. Enter High Dynamic Range (HDR). HDR imaging has been around for at least a couple of decades, but has been popularized more recently by new software tools.

Using one such tool, the Photomatix Pro HDR processing application, I tried again with the example image. On the same three bracketed exposures, I can produce this image:

This result better represents the feeling I had at the scene, represented by the title “Stormy Sunrise.” As a bonus, creating this image required no use of filters in the field, and minimal fine tuning work in Photoshop. The heavy lifting was done by “tone mapping,” a process of converting an HDR image back into an 8-bit or 16-bit image file that can be worked with conventionally (as the HDR image itself can not). In total it took less than 30 minutes of processing time after converting the RAW files – significantly less time than for the blended exposure version of the image, which honestly still needs more work.

The HDR image reveals more of the original scene’s drama than does the exposure blended version. More detail is visible throughout the sky, mountains and ice, in large part due to what are called local contrast enhancements – adjustments that emphasize tonal transitions and details within a very small space rather than strictly preserving the overall relationship of bright and dark tones across the entire image. Overall contrast and color tone is more expressive.

As for the original single frame with its middle-of-the-road, neutral exposure? While it could be tweaked, it is not remotely in the same league for expressing the impact of the original scene.

To see how you can use HDR as part of your workflow to create images with a large dynamic range, read on! This article gives a landscape photographer’s view of the theory behind HDR, describes how to capture the input images, and shows how to use two popular HDR tools: Photoshop CS2 and Photomatix Pro. It will also show how to use these tools to process both single frames and stitched panoramic images.

2. What is HDR?

Before getting into the tutorial, it would help to have some terms of reference. In brief, dynamic range (DR) is the range of luminance values from the darkest to the brightest. The original, real-world scene has a certain inherent DR which may be quite large – a ratio of 100,000:1 or more as DR is measured. Your eyes can perceive a subset of the scene’s DR (about 10,000:1), while your camera can record a smaller subset than your eyes can see – perhaps 400:1 for a DSLR. The DR of a monitor or a printed photograph is smaller yet.

High dynamic range (HDR) in photography means representing the full range of tonality present in the scene with high perceptual faithfulness. Most HDR techniques currently use software to combine several different exposures of a scene into a single file that maps the full range of luminance at every pixel. This HDR image is then processed in various ways depending on the ultimate usage. For most of us this means tone mapping the HDR image into a 16-bit or 8-bit digital file such as a JPEG or TIFF image.

If this is enough definition for you and you want to get into the part that shows how to get things done, feel free to skip ahead to the next section on shooting technique. The rest of this section provides the details of what HDR is for those who prefer to know “what” before getting into the “how”.

Key points covered in the rest of this section:

• Definition of HDR
• HDR vs. 8-bit or 16-bit file formats
• Capturing HDR images
• What is HDR good for?

Definition of HDR

Dynamic range (DR) is a fairly generic term used in a variety of disciplines. As described above, for our purposes in photography, DR is the range of luminance values from the darkest to the brightest. The DR of the real-world scene in front of you is the range of darkest to brightest portions available to your eye, film or imaging sensor. The DR of a camera is the subset of the scene’s DR that can be captured without being clipped on the highlight end, or reduced to noise or outright blocked up on the shadow end. Conversely, the DR of a monitor is the luminance range it can display from black to white.

High dynamic range (HDR) must mean a lot of DR. But how much is “a lot?” The standard unit for measuring luminance is candelas per square meter, or cd/m2. You may have seen this unit used in monitor specifications. According to the FAQ at www.hdrsoft.com (the web site for Photomatix Pro), “the luminance of starlight is around 0.001 cd/m2, that of a sunlit scene is around 100,000 cd/m2, [… and] the luminance of the sun itself is approximately 1,000,000,000 cd/m2.”

Without getting into the debates about which medium truly has precisely what DR, this chart summarizes some rule-of-thumb DR values for different stages of dealing with a scene:

One clear conclusion from this chart is that the experience of seeing the original scene, then capturing it, to reproducing it for others to see, is one of progressively losing DR. DR lost at capture time is gone for good, as it can never be regained after that point. If it can be captured as close as possible to what was present in the original scene, then perhaps something can be done to present the image to viewers with a better interpretation of the source scene’s tonality and detail.

Loosely speaking, then, HDR is the ability to capture and represent the full DR found in a scene with high perceptual accuracy and precision. To pin it down further, we need to look at digital file formats and how they represent luminance values.

Norman Koren’s web site has a good discussion of some of this information, specifically DR from digital capture through reproduction on screen or in print; see www.normankoren.com/digital_tonality.html. Sean McHugh’s web site also has a lot of good information about this subject; see for example www.cambridgeincolour.com/tutorials/dynamic-range.htm.

HDR vs. 8- or 16-bit Formats

An HDR image is represented using what can be considered a 32-bit per RGB channel format. The 32-bit numbers are decimal (or “floating point”) values, not integer values. The format records the luminosity of every point in the source scene, regardless of its level of brightness. There are several different HDR file formats in existence, including Radiance RGBE and Open-EXR. Each format encodes image data in a different way, with corresponding advantages and disadvantages.

Radiance RGBE and Open-EXR seem to be fairly dominant in terms of support in various applications. Both are supported by the tools discussed in this article. However the applications’ implementations of the formats are not necessarily compatible with one another.

Radiance RGBE was developed in the late 1980’s by Greg Ward as part of his Radiance imaging application, while Open-EXR was developed by Industrial Light and Magic and published as an open format around 2002. The key trade-off between the two formats appears to be that Radiance RGBE covers a much larger DR than Open-EXR, while Open-EXR offers more precision than Radiance RGBE. In truth both formats likely represent DR overkill for landscape shooting and most other forms of photography. But at least they provide the elbow room that is lacking in 8-bit and 16-bit formats.

The image formats with which we are all familiar, such as the JPEG and TIFF, supply a relatively small number of luminance values for each of the red, green and blue channels. Here is how the 8-bit, 16-bit, 32-bit Radiance RGBE and 32-bit Open-EXR formats break down:

Note that I am glossing over a few things here since this is not primarily a technical article. For example, the Radiance RGBE and Open-EXR file formats do not actually use 32 bits per channel in the file saved to disk, for storage size reasons. Also the DR ratios are not “apples-to-apples” comparisons since the darkest and lightest luminance values vary widely. I am also not attempting to address color space or gamma encoding which affect the image data encoded within the file. For more information on this topic, see the paper “High Dynamic Range Image Encodings” by Greg Ward.

There are several pragmatic benefits of the HDR formats over the 8-bit and 16-bit formats. When the source scene DR exceeds the luminance values the 8-bit and 16-bit formats can represent on either or both ends of the range, the DR is clipped. Shadows below the low end of the range block up to black (luminance value 0), while highlights above the top end of the range blow out to white (luminance value 255 or 65,535 depending on the format).

In addition, luminance values within the representable range must “snap to” the integral values within the format’s range. There is no way to represent a luminance value of 2.3 or 2.9, so these values both may be represented as 2. Thus, in addition to the range being limited potentially on both ends of the spectrum, the values it can represent are not necessarily very precise. Image processing functions that affect luminance may introduce increasing levels of error.

Finally, the integer luminance values are not mapped to the actual light from the source scene in a linear fashion. Some parts of the scene’s tonal range are compressed into fewer values while other parts of the scene’s range get a larger block of values. Thus the non-HDR format effectively applies a tone curve that biases the DR. Compressing part of the DR can cause issues like posterization and banding when image processing is done later.

In contrast (no pun intended), the HDR formats do not impose a practical limit on the DR that can be represented. (There is a limit, just not one that a landscape or indeed almost any type of photographer is likely to hit.) This is not only because HDR uses more bits to represent a wider range of luminance values, but also because these bits represent floating point values rather than integer values. Thus HDR can represent very small and very large luminance values, such as 0.00001 and 1,000,000,000 within the same file. And because the HDR formats have decimal precision, they can represent luminance values such as 2.3, 2.9 or 2.543635. Image processing functions that affect luminance introduce fewer errors.

Finally, the various HDR formats’ luminance values correspond linearly to the amount of light present at each point. There is no curve applied to compress part of the DR into a limited span of values. Posterization and banding are unlikely to occur when working with HDR files.

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