Embedded Signal Processing Laboratory,
Wireless Networking and Communications Group,
The University of Texas at Austin,
Austin, TX 78712 USA

debarati@utexas.edu -
bevans@ece.utexas.edu

Paper Draft - PowerPoint Slides - PDF Slides - Software Release

Related Resources: ESPL-LIVE HDR Image Database - Crowdsourced HDR IQA Study - No Reference HDR IQA Methods - PhD Dissertation on HDR IQA

- integrating local information-based pooling strategies in the TMQI IQA algorithm,
- measuring image naturalness by using mean-subtracted contrast-normalized pixels, and
- testing the proposed methods on JPEG compressed tone-mapped images and tone-mapped images for SDR displays using subjective scores.

**Answer**: Although we only used correlation-based IQA measures in this paper, we compute
root mean squared error (RMSE) RMSE values and reduced chi-squared test of subjective ratings
vs. predicted ratings in follow-up papers on HDR image quality assessment:

- D. Kundu,
D. Ghadiyaram,
A. C. Bovik and
B. L. Evans,
"No-Reference
Quality Assessment of High Dynamic Range Pictures",
*IEEE Transactions on Image Processing*, submitted July 3, 2016. - D. Kundu,
D. Ghadiyaram,
A. C. Bovik and
B. L. Evans,
"Large-scale
Crowdsourced Study for High Dynamic Range Pictures",
*IEEE Transactions on Image Processing*, submitted May 25, 2016.

*Question #2: How could you better handle noise in measurements and uncertainty in correlation coefficients?*

**Answer**: This paper uses two previous subjective evaluation studies of 20 participants
for tonempapping operators [Yeganeh & Wang, 2013] and 27 participants for compression
[Narwaria, Da Silva, Le Callet & Pepion, 2013].
With a smaller number of opinion scores, there can be a wider variability in the ability
of an automated algorithm to predict subjective results.
After we had submitted our ICIP 2016 paper in January 2016, we collaborated with Alan Bovik
and Deepti Ghadiyaram here at UT Austin to compile the
ESPL-LIVE HDR database
with over 1800 HDR images and obtain over 300,000 opinion scores from more than 5000 participants
in a crowdsourced study using Amazon Mechanical Turk.
Each HDR image has at least 40 evaluations.

*Question #3: How did you choose the parameters in the proposed naturalness measure?*

**Answer**: We used the parameters from [Yeganeh & Wang, 2013] as is.
We could have followed the approach in the [Yeganeh & Wang paper and trained the parameters
on a large set of natural images to determine the parameters in the naturalness measure.

*Question #4: What is the range of values for the proposed quality measure?*

**Answer**: The minimum value is 0. The maximum value is slightly greater than 1 due to
the *beta*^{delta1} term where *delta1* is 0.7088.
The parameter *beta* is the exponent of the fit to a generalized Gaussian density
of the mean subtracted contrast normalized pixels of the tonemapped image.
For natural images, *beta* can be greater than 1; in fact, *beta* commonly
takes values between 0.8 and 1.4.

Last Updated 10/01/16.