Gaze-Driven video streaming with saliency-based dual-stream switching

Abstract

The ability of a person to perceive image details falls precipitously with larger angle away from his visual focus. At any given bitrate, perceived visual quality can be improved by employing region-of-interest (ROI) coding, where higher encoding quality is judiciously applied only to regions close to a viewer's focal point. Straight-forward matching of viewer's focal point with ROI coding using a live encoder, however, is computation-intensive. In this paper, we propose a system that supports ROI coding without the need of a live encoder. The system is based on dynamic switching between two pre-encoded streams of the same content: one at high quality (HQ), and the other at mixed quality (MQ), where quality of a spatial region depends on its pre-computed visual saliency values. Distributed source coding (DSC) frames are periodically inserted to facilitate switching. Using a Hidden Markov Model (HMM) to model a viewer's temporal gaze movement, MQ stream is pre-encoded based on ROI coding to minimize the expected streaming rate, while keeping the probability of a viewer observing low quality (LQ) spatial regions below an application-specific ϵ. At stream time, the viewer's gaze locations are collected and transmitted to server for intelligent stream switching. In particular, server employs MQ stream only if: i) viewer's tracked gaze location falls inside the high-saliency regions, and ii) the probability that a viewer's gaze point will soon move outside high-saliency regions, computed using tracked gaze data and updated saliency values, is below ϵ. Experiments showed that video streaming rate can be reduced by up to 44%, and subjective quality is noticeably better than a competing scheme at the same rate where the entire video is encoded using equal quantization.