Abstract
The world is witnessing rapid transformations in hardware technology. This will keep on improving day by day. The processing power of every handheld device is significantly improved as well as the storage capacity is increased. With all these advancements, the personal video capture and usage of videos have tremendously increased across many applications. The quality assessment of personal videos has become very important. It is a vital task to design a model for assessing the video quality. A novel methodology for detecting damaged video frames is proposed here. The primary objective of the research is to detect uni-coloured frames and frames with ice effect. The novel histograms bin comparison technique is proposed for inter- and intraframe analysis. The shakiness of the video is calculated using motion estimation. The video quality is also assessed using blur detection as well as contrast calculation to spot useful portion in the video. The proposed framework generates the video quality metadata and supports the contextualisation process.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
