Evaluating Foveated Video Quality Using Entropic Differencing

TL;DR

This paper introduces FED, a novel full reference foveated image quality assessment method that uses entropic differencing to better predict human judgments of VR video quality, especially in bandwidth-efficient foveated compression.

Contribution

The paper presents FED, a new foveated image quality metric based on entropic differencing that outperforms existing algorithms in VR video quality assessment.

Findings

FED achieves state-of-the-art correlation with human judgments.

The algorithm effectively evaluates 2D and 3D VR videos.

FED is available as open-source software.

Abstract

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