Split & Dual Screen Comparison of Classic vs Object-based Video

Abstract

Over-the-top (OTT) streaming services like YouTube and Netflix induce massive amounts of video data, hereby putting substantial pressure on network infrastructure. This paper describes a demonstration of the object-based video (OBV) methodology that allows for the quality-variant MPEG-DASH streaming of respectively the background and foreground object(s) of a video scene. The OBV methodology is inspired by research into human visual attention and foveated compression, in that it allows to adaptively and dynamically assign bitrate to those portions of the visual scene that have the highest utility in terms of perceptual quality. Using a content corpus of interview-like video footage, the described demonstration proves the OBV methodology's potential to downsize video bitrate requirements while incurring at most marginal perceptual impact (i.e., in terms of subjective video quality). Thanks to its standards-compliant Web implementation, the OBV methodology is directly and broadly deployable without requiring capital expenditure.