Topic
Video optimization
About: Video optimization is a research topic. Over the lifetime, 75 publications have been published within this topic receiving 240 citations.
Papers
TL;DR: The study finds that greater availability of information in video titles is negatively associated with video views, whereas intensity of negative emotional sentiment in video title is positively associated withVideo views.
Abstract: YouTube's vast and engaged user base makes it central to firms' digital marketing effort. With extant studies focusing on viewers' post-view engagement behavior, however, research into what motivates viewers to click on and watch YouTube videos is scarce. This study investigates the implications of marketers' video optimization practices for video views on YouTube.,The study employed a data set of videos (N = 4,398) gathered by scraping YouTube's trending list. Using a combination of text and sentiment analysis, the study measured four video optimization practices: information content of video titles, emotional intensity of video titles, information content of video descriptions and volume of video tags. It then analyzed the effect of these video optimization practices on video views.,The study finds that greater availability of information in video titles is negatively associated with video views, whereas intensity of negative emotional sentiment in video titles is positively associated with video views. Further, greater availability of information in video descriptions is positively associated with video views. Finally, an inverted U-shaped relationship is found between volume of video tags and video views. Up to 17 video tags can contribute to more video views; however, beyond 17 tags, the relationship turns negative.,This study investigates the effect of marketers' video optimization practices on video views. While extant studies mainly focus on viewers' post-view engagement behavior, such as liking, commenting on and sharing videos, this study examines video views. Similarly, extant studies investigate videos' internal content, while this study investigates elements of the video metadata.
103 citations
Patent•
13 Mar 2012TL;DR: In this paper, a system and methods for modifying streaming data based on radio frequency information is provided, where the existing radio transceivers are split into a baseband unit and a remote radio head, radio frequency (RF) information including power levels, encoding, data rates, and bandwidth can be provided to video optimization server.
Abstract: System and methods for modifying streaming data based on radio frequency information is provided. As radio transceivers transition move to a shared resource or cloud model and the existing radio transceivers are split into a baseband unit and a remote radio head, radio frequency (RF) information including power levels, encoding, data rates, and bandwidth can be provided to video optimization server. The RF information can be provided more frequently to allow real-time modifications to streaming video data. Existing protocols are reactionary in nature and perceive changing channel conditions indirectly. By providing RF information from the baseband unit on a low latency channel, modifications to the video stream can be made before an impact would be noticed at the protocol level. Also, policy information can be used to influence the changes made to streaming data in addition to the RF information.
41 citations
TL;DR: Investigation of the impact of video file characteristics, video optimization techniques and differences in animal tracking algorithms on the accuracy of quantitative neurobehavioural endpoints indicates that variability in video file parameters can be a source of experimental biases in behavioural analysis.
Abstract: Chemobehavioural phenotypic analysis using small aquatic model organisms is becoming an important toolbox in aquatic ecotoxicology and neuroactive drug discovery. The analysis of the organisms' behavior is usually performed by combining digital video recording with animal tracking software. This software detects the organisms in the video frames, and reconstructs their movement trajectory using image processing algorithms. In this work we investigated the impact of video file characteristics, video optimization techniques and differences in animal tracking algorithms on the accuracy of quantitative neurobehavioural endpoints. We employed larval stages of a free-swimming euryhaline crustacean Artemia franciscana,commonly used for marine ecotoxicity testing, as a proxy modelto assess the effects of video analytics on quantitative behavioural parameters. We evaluated parameters such as data processing speed, tracking precision, capability to perform high-throughput batch processing of video files. Using a model toxicant the software algorithms were also finally benchmarked against one another. Our data indicates that variability in video file parameters; such as resolution, frame rate, file containers types, codecs and compression levels, can be a source of experimental biases in behavioural analysis. Similarly, the variability in data outputs between different tracking algorithms should be taken into account when designing standardized behavioral experiments and conducting chemobehavioural phenotyping.
33 citations
TL;DR: A novel 5G Video Optimizer Virtual Network Function (vOptimizerVNF) that leverages the latest technologies in 5G and video processing to address this important challenge of scalable and priorizable in-network video optimization schemes.
Abstract: The increasing popularity of video applications and ever-growing high-quality video transmissions (e.g., 4K resolutions), has encouraged other sectors to explore the growth of opportunities. In the case of health sector, mobile Health services are becoming increasingly relevant in real-time emergency video communication scenarios where a remote medical experts’ support is paramount to a successful and early disease diagnosis. To minimize the negative effects that could affect critical services in a heavily loaded network, it is essential for 5G video providers to deploy highly scalable and priorizable in-network video optimization schemes to meet the expectations of a large quantity of video treatments. This paper presents a novel 5G Video Optimizer Virtual Network Function (vOptimizerVNF) that leverages the latest technologies in 5G and video processing to address this important challenge. Advanced traffic filtering is coupled with Scalable H.265 video coding to enable run-time bandwidth-saving video optimization without compromising Quality of Service (QoS); kernel-space video processing is introduced to achieve further performance gains; and the use of a Virtual Network Function (VNF) facilitates dynamic deployment of virtualized video optimizers to achieve scalability and flexibility in this service. The proposed approach is implemented in a realistic 5G testbed and empirical results demonstrate the superior scalability and performance achieved.
32 citations
TL;DR: This work introduces VR-EXP, an open-source platform for carrying out VR video streaming performance evaluation, and is the first work to propose a systematic approach, accompanied by a software toolkit, which allows one to compare different optimization techniques under the same circumstances.
Abstract: To cope with the massive bandwidth demands of Virtual Reality (VR) video streaming, both the scientific community and the industry have been proposing optimization techniques such as viewport-aware streaming and tile-based adaptive bitrate heuristics. As most of the VR video traffic is expected to be delivered through mobile networks, a major problem arises: both the network performance and VR video optimization techniques have the potential to influence the video playout performance and the Quality of Experience (QoE). However, the interplay between them is neither trivial nor has it been properly investigated. To bridge this gap, in this article, we introduce VR-EXP, an open-source platform for carrying out VR video streaming performance evaluation. Furthermore, we consolidate a set of relevant VR video streaming techniques and evaluate them under variable network conditions, contributing to an in-depth understanding of what to expect when different combinations are employed. To the best of our knowledge, this is the first work to propose a systematic approach, accompanied by a software toolkit, which allows one to compare different optimization techniques under the same circumstances. Extensive evaluations carried out using realistic datasets demonstrate that VR-EXP is instrumental in providing valuable insights regarding the interplay between network performance and VR video streaming optimization techniques.
25 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2020 | 10 |
| 2019 | 18 |
| 2018 | 9 |
| 2017 | 7 |
| 2016 | 9 |
| 2015 | 6 |