Bhaskaran, Vasudev, 1956-Image and video compression standards : algorithms and architectures / by Vasudev Bhaskaran, Konstantinos Konstantinides. --2nd ed.p. cm. --(The Kluwer international series in engineering and computer science; SECS 408) Includes bibliographical references and index.

Abstract-We present an algorithm and a hardware architecture for block-based motion estimation that involves transforming video sequences from a multibit to a one-bit/pixel representation and then applying conventional motion estimation search strategies. This results in substantial reductions in arithmetic and hardware complexity and reduced power consumption, while maintaining good compression performance. Experimental results and a custom hardware design using a linear array of processing elements are also presented.

Digital video is becoming widely available in compressed form, such as a motion JPEG or MPEG coded bitstream. In applications such as video browsing or picture-in-picture, or in transcoding for a lower bit rate, there is a need to downscale the video prior to its transmission. In such instances, the conventional approach to generating a downscaled video bitstream at the video server would be to first decompress the video, perform the downscaling operation in the pixel domain, and then recompress it as, say, an MPEG bitstream for efficient delivery. This process is computationally expensive due to the motion-estimation process needed during the recompression phase. We propose an alternative compressed domain-based approach that computes motion vectors for the downscaled (N= N= N=22 2 2N= N= N=2) video sequence directly from the original motion vectors for the N 2 N N 2 N N 2 N video sequence. We further discover that the scheme produces better results by weighting the original motion vectors adaptively. The proposed approach can lead to significant computational savings compared to the conventional spatial (pixel) domain approach. The proposed approach is useful for video severs that provide quality of service in real time for heterogeneous clients.

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