5,360 research outputs found
No-reference image quality assessment through the von Mises distribution
An innovative way of calculating the von Mises distribution (VMD) of image
entropy is introduced in this paper. The VMD's concentration parameter and some
fitness parameter that will be later defined, have been analyzed in the
experimental part for determining their suitability as a image quality
assessment measure in some particular distortions such as Gaussian blur or
additive Gaussian noise. To achieve such measure, the local R\'{e}nyi entropy
is calculated in four equally spaced orientations and used to determine the
parameters of the von Mises distribution of the image entropy. Considering
contextual images, experimental results after applying this model show that the
best-in-focus noise-free images are associated with the highest values for the
von Mises distribution concentration parameter and the highest approximation of
image data to the von Mises distribution model. Our defined von Misses fitness
parameter experimentally appears also as a suitable no-reference image quality
assessment indicator for no-contextual images.Comment: 29 pages, 11 figure
Learned Perceptual Image Enhancement
Learning a typical image enhancement pipeline involves minimization of a loss
function between enhanced and reference images. While L1 and L2 losses are
perhaps the most widely used functions for this purpose, they do not
necessarily lead to perceptually compelling results. In this paper, we show
that adding a learned no-reference image quality metric to the loss can
significantly improve enhancement operators. This metric is implemented using a
CNN (convolutional neural network) trained on a large-scale dataset labelled
with aesthetic preferences of human raters. This loss allows us to conveniently
perform back-propagation in our learning framework to simultaneously optimize
for similarity to a given ground truth reference and perceptual quality. This
perceptual loss is only used to train parameters of image processing operators,
and does not impose any extra complexity at inference time. Our experiments
demonstrate that this loss can be effective for tuning a variety of operators
such as local tone mapping and dehazing
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