Assessing the perceptual quality of pictures still remains a difficult task even for humans. This is true, especially when
there are many interesting regions to look at (e.g. sea and foreground subject) or when the differences among the pictures
are subtle. Despite that, trends in user preference do exist and they can be a valuable source of information for designing
enhancement algorithms. However, a major problem is to assess preference trends and to translate them in an algorithm
with a formal methodology. The approach that we describe in this paper proposes a multi-step solution. In the first
instance we relate the space of possible enhancement sequences (intended as chain of enhancement algorithms) to the
content of the image and then reduce the number of sequences through an iterative selection penalizing the sequences
that produce artifacts or that generates close results. We then present the user with pairs of images enhanced with the
various sequences and we ask to select the best in each comparison. Finally, we perform a statistical analysis of users'
votes through a statistical method. Preliminary results show preference for saturated and colorful sea and sky and "de-saturated"
snow.
We introduce a novel algorithm for local contrast enhancement. The algorithm exploits a background image which
is estimated with an edge-preserving filter. The background image controls a gain which enhances important
details hidden in underexposed regions of the input image. Our designs for the gain, edge-preserving filter and
chrominance recovery avoid artifacts and ensure the superior image quality of our results, as extensively validated
by user evaluations. Unlike previous local contrast methods, ours is fully automatic in the sense that it can be
directly applied to any input image with no parameter adjustment. This is because we exploit a trainable decision
mechanism which classifies images as benefiting from enhancement or otherwise. Finally, a novel windowed TRC
mechanism based on monotonic regression ensures that the algorithm takes only 0.3 s to process a 10 MPix
image on a 3 GHz Pentium.
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