KEYWORDS: Transparency, Error analysis, LCDs, Electronic imaging, Visualization, Heads up displays, Virtual reality, Human vision and color perception, Psychology, Calibration
Our previous experiments with additive and multiplicative transparent text on textured backgrounds show that readability can be more accurately predicted by adjusting the contrast with a contrast-gain-like divisive factor that includes the background RMS contrast. However, the factor performed poorly at predicting readability differences on two different patterned backgrounds. Using the same images of the previous study we presented the target words alone and single letters cut out of the target words. We found that word identification and word discriminability was affected by the backgrounds in the same way that the paragraph search performance was affected, but that letter identifiability on these two backgrounds was predicted by the metric. We also found a significant improvement from including different contrast gains for positive and negative contrasts in the metric. Unfortunately, word readability is not necessarily simply related to letter identifiability and simple contrast measures.
Several discriminability measures were correlated with reading sped over a range of screen backgrounds. Reading speed was measured using a search task in which observers tried to find one of three works in a short paragraph of black text. There were four background patterns combined with three colors at two intensities. The text contrast had a small positive correlation with speed. Background RMS contrast showed a stronger, negative correlation. Text energy in the spatial frequency bands corresponding to lines and letters also showed strong relationships. A general procedure for constructing a masking index from an image discrimination model is described and used to generate two examples indices: a global masking index, based on a single filter model combining text contrast and background RMS contrast, and a spatial-frequency-selective masking index. These indices did not lead to better correlations than those of the RMS measures alone, but they should lead to better correlations when there are larger variations in text contrast and masking patterns.
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