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Technical Report

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Technical Report Number



In this thesis we have studied the potential of Motion JPEG 2000 for video processing and compared its performance with current widely used video coding standards, Motion JPEG, MPEG-2, and MPEG-4. Four key aspects are compared among them, which are compression efficiency, error resilience, perceptual video quality, and computation complexity. Our experiments show that Motion JPEG 2000 provides high compression performance, strong error resilience, good perceptual video quality, and acceptable computation complexity for real-time video processing. Together with a rich set of features inherited from JPEG 2000, such as resolution and quality scalability, and flexibility for editing, Motion JPEG 2000 has many advantages as a coding standard for video processing in numerous applications. In video processing applications, especially the applications with stringent time requirements, the execution speed of the codec is a key issue for the overall performance. To speed up the execution of JPEG 2000 rate allocation and packetization procedure utilizing the D-Heap data structure. Implemented in Jasper and tested on five reference images, the proposed algorithm provides a speedup for JPEG 2000’s rate allocation and packetization of 15.9 times on average, and enables an average overall speedup of 33% for JPEG 2000 encoding procedure. Video applications over the Internet are becoming increasingly popular because of the explosive growth of the internet. However, video packet loss due to network congestion can degrade video quality dramatically. In this thesis, we proposed an efficient transmission scheme for Motion JPEG 2000 video sequences over active IP networks. The simulation results show that it can gracefully adapt to network congestion and improve the quality of video transmission in congested IP networks substantially. Image segmentation is critical step in content-based image retrieval, object based video compression and indexing, and object motion tracking. In this thesis, we have presented a hierarchical image representation method that preserves the spatial relationships between segments, and an image segmentation algorithm that efficiently constructs this representation. That experimental results show that this algorithm can segment images and extract objects precisely and efficiently.


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