ESPL Synthetic Image Database
Brian L. Evans
The University of Texas at Austin, Austin, TX USA
Visual Quality Assessment of Computer Graphics Images
Ms. Debarati Kundu
This work aims at analyzing the statistical properties of computer generated
images by application of the Natural Scene Statistics (NSS) models.
For the purpose of validation of the usefulness of NSS in this domain,
we have compiled the ESPL synthetic image database, which contains high
quality synthetic color images from the Internet, mostly 1920 x 1080 pixels.
The images are primarily chosen from popular video games and animation movies.
Some video games which are considered are multiplayer role playing games
(for example, War of Warcraft, MegaGlest), first person shooter games
(such as Counter Strike), bike and car racing games, and games with more
realistic content, such as FIFA.
Some of the animation movies, from which the images were collected are,
The Lion King, the Tinkerbell series, Avatar, The Beauty and the Beast,
Monster series, Ratatouille, the Cars series etc.
Care has been taken to provide as varied a range of interest as possible,
by incorporating both natural and non-photorealistic renderings of human
figures, human-made objects, fantasy figures such as fairies and monsters,
close up shots, wide angle shots, images showing both high and low degrees
of color saturation, and background textures with no foreground object.
In addition to the pristine images, we have considered some distortions,
in order to simulate the artifacts introduced to the image while rendering,
or transmission over a network.
Each image has been degraded using five levels of each type of distortion,
ranging from just visible artifacts, to the artifacts which are visible
to a large extent.
Some of the distortions considered are described below:
- High Frequency noise: Noise is perhaps the most pervasive
distortion present in images, be it natural or synthetic.
The synthetic image may get corrupted with noise, either during rendering
(for example, in case of any random sample based methods, such as photon
mapping based on Monte-Carlo techniques), or during transmission over a network.
Three types of noise, namely Gaussian noise, Salt-and-Pepper noise, and
Speckle noise, have been considered.
- Interpolation: Artifact results from insufficient super sampling,
or lack of an anti-aliasing filter, and is caused by downsampling the reference
image, and upsampling to the original resolution using an interpolation method.
These effects are primarily visible at edges, such as shadow maps, and the users
see them as jaggedness.
- Banding: Banding artifact is an unintended side effect of color
quantization, which can affect both natural and synthetic images.
Sometimes, the bit depth might be insufficient to accurately sample a continuous
gradation of a color tone.
Hence, this continuous gradation might appear as a series of discrete bands of color.
This becomes especially prominent in the large smooth textureless regions of the
rendered image, such as the sky.
- Ringing: Ringing artifact is a type of low frequency noise, which
arises as a spurious signal or "ghosting" at the edges in the images.
For this database, this artifact was introduced by filtering the image with a
two-dimensional sinc function, with varying frequencies, and truncated to a
certain window size.
Low frequency noise can arise in many photon mapping processes.
- Gaussian Blur: Images may be blurred as an effect introduced by many
denoising techniques, which tend to remove the Gaussian noise with low pass
filtering with a Gaussian kernel.
In synthetic images, in order to give the shadows a realistic soft edge, they
might be filtered using a Gaussian kernel, but overdoing the filter operation
can introduce visible blurring distortions.
- JPEG compression: The blocking artifacts appear when a low compression
ratio is chosen for JPEG coded images, especially under low network bandwidth
This artifact can be encountered in the scenario of cloud gaming, where the
rendered game image might be transmitted to the users playing using a
low bandwidth network.
Here are the releases of the ESPL Synthetic Image Database:
- Version 2.0, Jan. 30, 2015.
25 reference images taken from version 1.0 plus five
types of distortion for each image and four levels of distortion
for each distortion type, for a total of 525 images.
A description of the database, and a statistical analysis of
23 full-reference and 17 no-reference image quality assessment
algorithms vs. subjective tests, are contained in the download
and also available in .
- Version 1.0, Apr. 21, 2014.
222 reference images plus 9 types of distorted images for each
image and 5 levels of distortion for each distortion type, for
a total of 10,212 images.
A description of the database and a statistical analysis of
the images contained therein are in the download and also
available in .
Please contact Debarati Kundu to
request the password of the zipped archive.
In the e-mail message, please mention your name, affiliation, and intended
use of the database.
The database is available free of charge.
The images were primarily collected from publicly available wallpaper
Web sites, such as
The database also includes two images from the 'Fairy Forest' sequence, found in
Utah 3D Animation Repository.
All images are copyright of their rightful owners, and the authors do not
claim ownership. No copyright infringement is intended.
The database is meant to be used strictly for non-profit educational purposes.
To report an image or request its immediate removal, please copy the image URL from
your browser, and email it to us, indicating your real name and contact information.
IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS DATABASE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF TEXAS AT AUSTIN HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
THE UNIVERSITY OF TEXAS AT AUSTIN SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF TEXAS AT AUSTIN HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
- D. Kundu and
B. L. Evans,
"Spatial Domain Synthetic Scene Statistics",
Proc., Asilomar Conf. on Signals, Systems, and Computers,
Nov. 2-5, 2014, Pacific Grove, CA USA.
- D. Kundu and
B. L. Evans,
"Full-Reference Visual Quality Assessment for Synthetic Images: A Subjective Study",
Proc. IEEE Conf. on Image Processing,
Sep. 27-30, 2015, Quebec City, Canada.
Won a Top 10% Paper Award.
- D. Kundu and
B. L. Evans,
""No-reference Synthetic Image Quality Assessment using Scene Statistics",
Proc. Asilomar Conf. on Signals, Systems and Computers,
Nov. 8-11, 2015, Pacific Grove, CA USA.
- D. Kundu,
L. K. Choi,
A. C. Bovik and
B. L. Evans,
"Subjective and Objective Quality Evaluation of Lightly Distorted Synthetic Images",
IEEE Transactions on Image Processing,
submitted May 11, 2016.
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