Toward Generalized Psychovisual Preprocessing For Video Encoding

• Deep perceptual preprocessing has recently emerged as a new way to enable further bitrate savings across several generations of video encoders without breaking standards or requiring any changes in client devices. In this paper, we lay the foundations toward a generalized psychovisual preprocessing framework for video encoding and describe one of its promising instantiations that is practically deployable for video-on-demand, live, gaming and user-generated content. Results using state-of-the-art AVC, HEVC and VVC encoders show that average bitrate (BD-rate) gains of 11% to 17% are obtained over three state-of-the-art reference-based quality metrics (Netflix VMAF, SSIM and Apple AVQT), as well as the recently-proposed non-reference ITU-T p.1204 metric. The runtime complexity of the proposed framework on CPU is shown to be equivalent to a single x264 medium-preset encoding. On GPU hardware, our approach achieves 260fps for 1080p video (below 4ms/frame), thereby enabling its use in very-low latency live video or game streaming applications.

ATSC 3.0 as a use Case for Public Safety Communications

• Fire and EMS services across the United States still rely on paging technology to communicate emergency incident information. The infrastructure for these paging systems is typically owned, operated, and maintained by the local government or agency to ensure coverage includes as close to 100% of the jurisdiction as possible. This paper proposes the use of ATSC 3.0 datacasting technology to serve the paging needs of public safety and uses North Carolina as a test case. This concept could lead to cost-sharing, greater collaboration across jurisdictions, and reduced response times for mutual aid requests. The public deserve the best possible response from the public safety sector and therefore, public safety deserves the best technology available in order to achieve their mission.