IEEE Transactions on Image Processing, submitted May 25, 2016, and resubmitted Dec. 18, 2016.

Large-scale Crowdsourced Study for Tone Mapped HDR Pictures

Debarati Kundu (1), Deepti Ghadiyaram (2), Alan C. Bovik (2) and Brian L. Evans (1)

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

(2) Laboratory for Image and Video Engineering, Wireless Networking and Communications Group, The University of Texas at Austin, Austin, TX 78712 USA -

Paper Draft

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


Measuring digital picture quality, as perceived by human observers, is increasingly important in many applications in which humans are the ultimate consumers of visual information. Standard dynamic range (SDR) images provide 8 bits/color/pixel. High dynamic range (HDR) images, usually created from multiple exposures of the same scene, can provide 16 or 32 bits/color/pixel, but need to be tonemapped to SDR for display on standard monitors. Multi-exposure fusion (MEF) techniques bypass HDR creation by fusing on exposure stack directly to SDR images to achieve aesthetically pleasing luminance and color distributions. Many HDR and MEF databases have a relatively small number of images and human opinion scores, obtained under stringently controlled conditions, thereby limiting realistic viewing. Moreover, many of these databases are intended to compare tone-mapping algorithms, rather than being specialized for developing and comparing IQA models. To overcome these challenges, we conducted a massively crowdsourced online subjective study. The primary contributions described in this paper are
  1. the new ESPL-LIVE HDR Image Database that we created containing diverse images obtained by tone-mapping operators (TMO) and MEF algorithms, with and without post processing;
  2. a large-scale subjective study that we conducted using a crowdsourced platform to gather more than 300,000 opinion scores on 1,811 images from over 5,000 unique observers; and
  3. a detailed study of the correlation performance of state-of-the-art no-reference image quality assessment algorithms against human opinion scores of these images.
The database is available at:

Note: IQA means Image Quality Assessment.

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 scientific work. 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 copyrights.

Last Updated 12/19/16.