Video summarization is the method of extracting key frames or clips from a video to generate a synopsis of the content of the video. Generally, video is compressed before storing or transmitting it in most of the practical applications. Traditional techniques require the videos to be decoded to summarize them, which is a tedious job. Instead, compressed domain video processing can be used for summarizing videos by partially decoding them. A classification and analysis of various summarization techniques are presented in this article with special focus on compressed domain techniques along with a discussion on machine-learning-based techniques that can be applied to summarize the videos.<?vsp -1.2pt?>
Background:
Multimedia transmission over wireless communication is gaining
momentum with rapid use of mobile hand-held devices. Providing a QoS based routing solution is a
major challenge, due to the transient and inaccurate state of Mobile Ad hoc Networks. Discovering
optimal multicast routes is an NP-Problem and hence, QoS based routing is typically an optimization
problem. Swarm Intelligence is a heuristic-based approach to find solutions to various complex
problems using the principle of collective behaviour of natural agents.
Objective:
An ACO based approach for optimization of QoS based multicast routing algorithm for
multimedia streaming applications is proposed. Proposed approach performed well in comparison to
other state-of-the-art approaches with respect to path maintenance, packet delivery ratio, and end-toend
delay.
Methods:
The multicast routing model is simulated as a tree structure, where the nodes represent
stations and the edges represent the link between the stations.
Results:
Results show that proposed approach is much faster in convergence speed than the
conventional AntNet. With the increasing size of the MANET environment, the convergence time of
proposed approach is much better than AntNet. This is mainly due to the trace maintenance, treebased
approach for path selection and implementation of local update and global update of the
pheromone values.
Conclusion:
We can conclude that the proposed approach is a more effective algorithm for multiconstraints
multicast routing.
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