Embedded multimedia systems provide real-time video support for applications in entertainment (mobile phones, internet video websites), defense (video-surveillance and tracking) and public-domain (tele-medicine, remote and distant learning, traffic monitoring and management). With the widespread deployment of such real-time embedded systems, there has been an increasing concern over the security and authentication of concerned multimedia data.While several (software) algorithms and hardware architectures have been proposed in the research literature to support multimedia security, these fail to address embedded applications whose performance specifications have tighter constraints on computational power and available hardware resources.The goals of this dissertation research are two fold:1. To develop novel algorithms for joint video compression and encryption. The proposed algorithms reduce the computational requirements of multimedia encryption algorithms. We propose an approach that uses the compression parameters instead of compressed bitstream for video encryption.2. Hardware acceleration of proposed algorithms over reconfigurable computing platforms such as FPGA andover VLSI circuits. We use signal processing knowledge to make the algorithms suitable for hardware optimizations and try to reduce the critical path of circuits using hardware-specific optimizations.The proposed algorithms ensures a considerable level of security for low-power embedded systems such as portable video players and surveillance cameras. These schemes have zero or little compression losses and preserve the desired properties of compressed bitstream in encrypted bitstream to ensure secureand scalable transmission of videos over heterogeneous networks.They also support indexing, search and retrieval in secure multimedia digital libraries. This property is crucial not only for police and armed forces to retrieve information about a suspect from a large video database of surveillance feeds, but extremely helpful for data centers (such as those used by youtube, aol and metacafe) in reducing the computation cost in search and retrieval of desired videos.
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