  Virtual Reality is regaining attention due to recent advancements in hardware
technology. Immersive images / videos are becoming widely adopted to carry
omnidirectional visual information. However, due to the requirements for higher
spatial and temporal resolution of real video data, immersive videos require
significantly larger bandwidth consumption. To reduce stresses on bandwidth,
foveated video compression is regaining popularity, whereby the space-variant
spatial resolution of the retina is exploited. Towards advancing the progress
of foveated video compression, we propose a full reference (FR) foveated image
quality assessment algorithm, which we call foveated entropic differencing
(FED), which employs the natural scene statistics of bandpass responses by
applying differences of local entropies weighted by a foveation-based error
sensitivity function. We evaluate the proposed algorithm by measuring the
correlations of the predictions that FED makes against human judgements on the
newly created 2D and 3D LIVE-FBT-FCVR databases for Virtual Reality (VR). The
performance of the proposed algorithm yields state-of-the-art as compared with
other existing full reference algorithms. Software for FED has been made
available at: http://live.ece.utexas.edu/research/Quality/FED.zip
