The Consistency of Visual Search Models on High Dynamic Range and Tone Mapped Images

EasyChair Preprint 1876

Abstract

Tone mapping operators (TMO) are compression algorithms that compress the bit depth of high dynamic range (HDR) images in such a way that when the tone mapped version of the HDR image is shown on a low dynamic range screen large portions of the image don’t appear over/under-exposed. But the process of reducing the bit-depth of an image often alters the appearance of the image, due to the loss of information and the introduction of artifacts, changing the gaze patterns when people look at tone mapped images. Saliency based TMO’s aim to compress the bit-depth of an HDR image, while trying to keep the most salient locations in the HDR image salient after tone mapping. However, these models don’t ensure that the saliency model used is actually able to predict eye gaze in HDR environments accurately and assess how eye gaze is influenced by artifacts introduced into the image by the compression process. In this paper we evaluate a suite of saliency models against 8 well-known TMO’s and the original HDR image to see how well each saliency model can predict the change in eye gaze when different TMO are applied. By doing this we can develop a firm basis on which to develop a saliency influenced tone mapping model to both compress HDR images and influence attention within the tone mapped image.

Keyphrases: high dynamic range, image processing, tone mapping, visual salience, visual search

@booklet{EasyChair:1876,
  author    = {Andre Harrison and Michael Green and Chou Hung and Adrienne Raglin},
  title     = {The Consistency of Visual Search Models on High Dynamic Range and Tone Mapped Images},
  howpublished = {EasyChair Preprint 1876},
  year      = {EasyChair, 2019}}