Proc. Asilomar Conference on Signals, Systems and Computers,
Nov. 8-11, 2015, Pacific Grove, CA USA.
No-reference Synthetic Image Quality Assessment using 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 -
Table I: Correlation Scores -
ESPL Synthetic Image Database
Measuring visual quality, as perceived by human observers,
is becoming increasingly important in many applications
where humans are the ultimate consumers of visual information.
Significant progress has been made for assessing the subjective
quality of natural images, such as those taken by optical cameras.
Natural Scene Statistics (NSS) is an important tool for
no-reference visual quality assessment of natural images,
where the reference image is not needed for comparison.
In this paper, we take an important step towards using NSS to
automate visual quality assessment of photorealistic synthetic
scenes typically found in video games and animated movies.
Our primary contributions are
We find that similar to natural scenes, synthetic scene
statistics can be successfully used for IQA and certain
statistical features are good for certain image distortions
- conducting subjective tests on our publicly available
ESPL Synthetic Image Database containing 500 distorted
images (20 distorted images for each of the 25 original
images) in 1920 x 1080 format, and
- evaluating the performance of 17 no-reference
image quality assessment (IQA) algorithms using synthetic
COPYRIGHT NOTICE: All the documents on this server
have been submitted by their authors to scholarly journals or conferences
as indicated, for the purpose of non-commercial dissemination of
The manuscripts are put on-line to facilitate this purpose.
These manuscripts are copyrighted by the authors or the journals in which
they were published.
You may copy a manuscript for scholarly, non-commercial purposes, such
as research or instruction, provided that you agree to respect these
Last Updated 11/10/15.