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Computer Science, Computer Vision and Pattern Recognition, cs.CV
Abstract:
Light fields become a popular representation of three dimensional scenes, and
there is interest in their processing, resampling, and compression. As those
operations often result in loss of quality, there is a need to quantify it. In
this work, we collect a new dataset of dense reference and distorted light
fields as well as the corresponding quality scores which are scaled in
perceptual units. The scores were acquired in a subjective experiment using an
interactive light-field viewing setup. The dataset contains typical artifacts
that occur in light-field processing chain due to light-field reconstruction,
multi-view compression, and limitations of automultiscopic displays. We test a
number of existing objective quality metrics to determine how well they can
predict the quality of light fields. We find that the existing image quality
metrics provide good measures of light-field quality, but require dense
reference light- fields for optimal performance. For more complex tasks of
comparing two distorted light fields, their performance drops significantly,
which reveals the need for new, light-field-specific metrics.
