MPEG (Video coding standard)

Lower prices of video sensors, security concerns and the need for better and faster
algorithms to extract high level information from video sequences are all factors which
have stimulated research in the area of automated video surveillance systems. In the
context of security the analysis of human interrelations and their environment provides
hints to proactively identify anomalous behavior. However, human detection is a
necessary component in systems where the automatic extraction of higher level
information, such as recognizing individuals' activities, is required. The human detection
problem is one of classification. In general, motion, appearance and shape are the
classification approaches a system can employ to perform human detection. Techniques
representative of these approaches, such us periodic motion detection, skin color
detection and MPEG-7 shape descriptors are implemented in this work. An infrastructure
that allows data collection for such techniques was also implemented.

The field of Video Transcoding has been evolving throughout the past ten years. The need for transcoding of video files has greatly increased because of the new upcoming standards which are incompatible with old ones. This thesis takes the method of using machine learning for video transcoding mode decisions and discusses ways to improve the process of generating the algorithm for implementation in different video transcoders. The transcoding methods used decrease the complexity in the mode decision inside the video encoder. Also methods which automate and improve results are discussed and implemented in two different sets of transcoders: H.263 to VP6 , and MPEG-2 to H.264. Both of these transcoders have shown a complexity loss of almost 50%. Video transcoding is important because the quantity of video standards have been increasing while devices usually can only decode one specific codec.

This paper presents the results of the 3DTV quality evaluation on autostereoscopic displays using asymmetric view coding. Asymmetric view coding encodes the stereo views with different quality. It has been shown that the human visual system is able to compensate for this asymmetric view quality and present a good quality 3D video. Asymmetric video coding can be exploited to reduce the bandwidth requirements for 3DTV services. The key factors that affect the asymmetric video coding are the compression algorithms, the human visual system, and the 3D display.