Since the middle of the 20th century the technological development of conventional photographic cameras has taken advantage of the advances in electronics and signal processing. One speci c area that has bene ted from these developments is that of auto-focus, the ability for a cameras optical arrangement to be altered so as to ensure the subject of the scene is in focus. However, whilst the precise focus point can be known for a single point in a scene, the method for selecting a best focus for the entire scene is an unsolved problem. Many focus algorithms have been proposed and compared, though no overall comparison between all algorithms has been made, nor have the results been compared with human observers. This work describes a methodology that was developed to benchmark focus algorithms against human results. Experiments that capture quantitative metrics about human observers were developed and conducted with a large set of observers on a diverse range of equipment. From these experiments, it was found that humans were highly consensual in their experimental responses. The human results were then used as a benchmark, against which equivalent experiments were performed by each of the candidate focus algorithms. A second set of experiments, conducted in a controlled environment, captured the underlying human psychophysical blur discrimination thresholds in natural scenes. The resultant thresholds were then characterised and compared against equivalent discrimination thresholds obtained by using the candidate focus algorithms as automated observers. The results of this comparison and how this should guide the selection of an auto-focus algorithm are discussed, with comment being passed on how focus algorithms may need to change to cope with future imaging techniques.
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