Proc. Asilomar Conference on Signals, Systems and Computers,
Nov. 2-5, 2014, pp. 948-954, Pacific Grove, California USA.
Spatial Domain Synthetic Scene Statistics
Brian L. Evans
Embedded Signal Processing Laboratory,
Wireless Networking and Communications Group,
The University of Texas at Austin,
Austin, TX 78712 USA
Paper Draft -
Poster Presentation -
ESPL Synthetic Image Database
Natural Scene Statistics (NSS) has been applied to natural images
obtained through optical cameras for automated visual quality
Since NSS does not need a reference image for comparison, NSS has
been used to assess user quality-of-experience, such as for
streaming wireless image and video content acquired by cameras.
In this paper, we take an important first step in using NSS to
automate visual quality assessment of synthetic images found in
video games and animated movies.
In particular, we analyze NSS for synthetic images in the spatial
domain using mean-subtracted-contrast-normalized (MSCN) pixels
and their gradients.
The primary contributions of this paper are
We find that similar to the case for natural images, the distributions
of the MSCN pixels for synthetic images can be modeled closely by
Generalized Gaussian and Symmetric Alphha Stable distributions, with
slightly different shape and scale parameters.
- creation of a publicly available ESPL Synthetic Image database,
containing 221 color images, mostly in high definition resolution
of 1920 x 1080, and
- analysis of the statistical distributions of the MSCN coefficients
(and their gradients) for synthetic images, obtained from the
Question and Answer Session
The following is a reconstruction by the first author of the
questions and answers during the poster presentation:
- Question #1: How do you define artifact in a synthetic image?
The fact that it is an artificial image might itself be an
artifact according to some viewers.
- Answer #1: We are concerned more about the visual quality
Unrealistic content does not necessarily mean a visually
We are trying to judge image quality without taking into
account the content.
- Question #2: Isn't it natural that MSCN coefficient
distribution of photorealistic graphics images will be same as
- Answer #2:
Yes, it is natural.
But till date there has been no analysis on a large database
to prove the amount of model match.
Also, we can have two images having identical MSCN coefficient
distribution, but one of them is a natural image and the other
one is a highly non-realistic graphics image.
We are trying to show that the distributions are valid irrespective
of the content, be it natural or artificial, as long as the
images are devoid of distortions.
- Question #3:
Is your database representative enough?
- Answer #3:
We have tried to maintain a good variation in content by looking
at images from a large number of animation movies and video games.
As in the case of any supervised learning approach, it is impractical
to take into account every graphics image ever rendered, but we have
tried to maintain a variety in illumination conditions, natural and
man made objects, single (or multiple) salient objects, clear (or not)
foreground-background separation, focus at different depths etc.
- Question #4:
How do the statistics vary with respect to change of size of the
- Answer #4:
We tried 3x3, 5x5, 7x7, and 9x9 local windows.
Statistics were pretty much the same; i.e., they led to nearly
identical distribution of the MSCN coefficients.
We reported the results with 7x7 windows in the paper.
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