ESPL Synthetic Image Database

Prof. Brian L. Evans
The University of Texas at Austin, Austin, TX USA

Visual Quality Assessment of Computer Graphics Images

Ph.D. Student: 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 221 high quality synthetic color images from the Internet, mostly 1920X1080 pixels in size.

The images are mainly 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, man- made objects, fantasy figures like fairies, and monsters, close up shots, wide angle shots, images showing both high and low degrees of color saturation, 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:

  • 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.
  • Aliasing: Aliasing 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 some interpolation scheme. These effects are primarily visible at the edges, such as shadow maps, and the users see them as 'jaggies'.
  • 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 conditions. 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.

A description of the synthetic image database and a statistical analysis of the images contained therein are available in [1].

Please click here to download the database. 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 images were primarily collected from publicly available wallpaper websites, such as www.hdwallpap.com and www.lbc9.com. 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.

References

  1. D. Kundu and B. L. Evans, "Spatial Domain Synthetic Scene Statistics", Asilomar Conf. on Signals, Systems, and Computers, Nov. 2-5, 2014, Pacific Grove, CA USA.


Mail comments about this page to bevans@ece.utexas.edu.