Humans see only a tiny region at the center of their visual field with the highest visual acuity, a behavior known as foveation. Visual acuity reduces drastically towards the visual periphery. ‘Foveated’ video coding/compression techniques exploit this non-uniformity to gain significant effi- ciency by compressing more in the periphery and less in the center. We propose a practical and scalable method to use such a technique for video streaming service over the Internet. The essential idea is to use a commodity webcam on the user side to provide real-time gaze feedback to the server with the server sending appropriately coded video to the client player. We develop a multi-resolution video coding approach that is scalable in that it is possible to pre-code the video in a small number of copies for a given set of resolutions. The coding approach is designed to match the error performance of an eye tracker built using commodity webcams. We demonstrate that the technique is energy efficient and thus usable in mobile devices. We develop a methodology for performance evaluation of such a system when network budgets may vary and video quality may fluctuate. Finally, we present a comprehensive user study that demonstrates a bandwidth reduction of a factor of 2 for the same user satisfaction.
The left is 240p video (B4) whlie the right is its corresponding foveated video (F4) playing within the same network bandwidth. Please carefully look at the face of Jennifer Lawrence. In foveated video streaming, the pixels around her face is streaming as high-resolution so that the user may not able to feel the low quality of the video.
The work was partially supported by NSF grants IIS- 1161876 and CNS-1405740, and the SUBSAMPLE project of the DIGITEO Institute France