Illumination-Guided Video Composition via Gradient Consistency Optimization

Jingye Wang; Bin Sheng; Ping Li; Yuxi Jin; David Dagan Feng

doi:10.1109/tip.2019.2916769

Illumination-Guided Video Composition via Gradient Consistency Optimization

IEEE Transactions on Image Processing ◽

10.1109/tip.2019.2916769 ◽

2019 ◽

Vol 28 (10) ◽

pp. 5077-5090

Author(s):

Jingye Wang ◽

Bin Sheng ◽

Ping Li ◽

Yuxi Jin ◽

David Dagan Feng

Keyword(s):

Video Composition

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User-Generated Video Composition Based on Device Context Measurements

International Journal of Semantic Computing ◽

10.1142/s1793351x17400049 ◽

2017 ◽

Vol 11 (01) ◽

pp. 65-84 ◽

Cited By ~ 1

Author(s):

Denny Stohr ◽

Iva Toteva ◽

Stefan Wilk ◽

Wolfgang Effelsberg ◽

Ralf Steinmetz

Keyword(s):

User Study ◽

Visual Analysis ◽

User Behavior ◽

Context Aware ◽

Video Streams ◽

Video Composition ◽

Video Recordings ◽

Quality Aspects ◽

Monitoring Service ◽

View Change

Instant sharing of user-generated video recordings has become a widely used service on platforms such as YouNow, Facebook.Live or uStream. Yet, providing such services with a high QoE for viewers is still challenging, given that mobile upload speed and capacities are limited, and the recording quality on mobile devices greatly depends on the users’ capabilities. One proposed solution to address these issues is video composition. It allows to switch between multiple recorded video streams, selecting the best source at any given time, for composing a live video with a better overall quality for the viewers. Previous approaches have required an in-depth visual analysis of the video streams, which usually limited the scalability of these systems. In contrast, our work allows the stream selection to be realized solely on context information, based on video- and service-quality aspects from sensor and network measurements. The implemented monitoring service for a context-aware upload of video streams is evaluated in different network conditions, with diverse user behavior, including camera shaking and user mobility. We have evaluated the system’s performance based on two studies. First, in a user study, we show that a higher efficiency for the video upload as well as a better QoE for viewers can be achieved when using our proposed system. Second, by examining the overall delay for the switching between streams based on sensor readings, we show that a composition view change can efficiently be achieved in approximately four seconds.

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