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This article explores how some different HDR imaging alike techniques can be adopted right into the audio domain.

The early adopters – game developers

In the lately cross-linked article “Finding Your Way With High Dynamic Range Audio In Wwise” some good overview was given on how the HDR concept was already adopted by some game developers over the recent years. Mixing in-game audio has its very own challenge which is about mixing different arbitrary occurring audio events in real-time when the game is actually played. Opposed to that and when we do mix off-line (as in a typical song production) we do have a static output format and don’t have such issues of course.

So it comes as no surprise, that the game developer approach turned out to be a rather automatic/adaptive in-game mixing system which is capable of gating quieter sources depending on the overall volume of the entire audio plus performing some overall compression and limiting. The “off-line mixing audio engineer” can always do better and if a mix is really too difficult, even the arrangement can be fixed by hand during the mixing stage.

There is some further shortcoming and from my point of view that is the too simplistic and reduced translation from “image brightness” into “audio loudness” which might work to some extend but since the audio loudness race has been emerged we already have a clear proof how utterly bad that can sound at the end. At least, there are way more details and effects to be taken into account to perform better concerning dynamic range perception.

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This comprehensive and in-depth article about HDR imaging was written by Sven Bontinck, a professional photographer and a hobby-musician.

A matter of perception.

To be able to use HDR in imaging, we must first understand what dynamic range actually means. Sometimes I notice people mistake contrast in pictures with the dynamic range. Those two concepts have some sort of relationship, but are not the same. Let me start by explaining in short how humans receive information with our eyes and ears. This is important because it influences the way we perceive what we see and hear and how we interpret that information.

We all know about the retina in our eyes where we find the light-sensitive sensors, the rods and cones. The cones provide us daytime vision and the perception of colours. The rods allow us to see low-light levels and provide us black-and-white vision. However there is a third kind of photoreceptors, the so-called photosensitive ganglion cells. These cells give our brain information about length-of-day versus length-of-night duration, but also play an important role in the pupillary control. Every sensor need a minimum amount of incitement to be able to react. At the same time all kind of sensors have a maximum amount that they may be exposed to. Above that limit, certain protection mechanisms start interacting to prevent damage occurring to the sensors.

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Back in time when I was at university, my very first DSP lectures were actually not about audio but image processing. Due to my interest in photography I followed this amazing and ever evolving domain over time. Later on, High Dynamic Range (HDR) image processing emerged and beside its high impact on digital photography, I immediately started to ask myself how such techniques could be translated into the audio domain. And to be honest, for quite some time I haven’t got a clue.

This image shows a typical problem digital photography still suffers from: The highlights are completely washed out and so the lowlights are turning into black abruptly w/o containing further nuances  – the dynamic range performance is pretty much poor and this is actually not what the human eye would perceive since it features both: a higher dynamic range per se but also a better adoption to different (and maybe difficult) lighting conditions.

On top, we have to expect severe dynamic range limitations in the output entities whether that’s a cheap digital print, a crappy TFT display or the limited JPG file format, just as an example. Analog film and prints does have such problems in principle also but not to that much extend since they typically offer more dynamic resolution and the saturation behavior is rather soft unlike the digital hard clipping. And this is where HDR image processing chimes in.

It typically distinguishes between single- and multi-image processing. Within multi-image processing, a series of Low Dynamic Range (LDR) images are taken in different exposures and combined into one single new image which contains an extended dynamic range (thanks to some clever processing). Afterwards, this version is rendered back into an LDR image by utilizing special  “tone mapping” operators which are performing a sort of dynamic range compression to obtain a better dynamic range impression but now in a LDR file.

Within single-image processing, there must be one single HDR image already available and then just tone mapping is applied. As an example, the picture below takes advantage of single-image processing from a RAW file which typically does have much higher bit-depth (12 or even 14 bit as of todays sensor tech) opposed to JPG (8 bit). As a result a lot of dynamic information can be preserved even if the output file still is just a JPG. As an added sugar, such a processed image also translates way better over a wide variety of different output devices, displays and viewing light conditions.