Includes bibliographical references. / The Wavelet Transform has been shown to be highly effective in image coding applications. This thesis describes the development of a new wavelet-based video compression algorithm which is based on the 3D wavelet transform, and requires no complicated motion estimation techniques. The proposed codec processes a sequence of images in a group of frames (GOF) by first transforming the group spatially and temporally, in order to obtain a GOF of 3D approximation and detail coefficients. The codec uses selective prediction of temporal approximation coefficients in order to decorrelate transformed GOFs. Following this, a modified version of Said and Pearlman's image coding technique of Set Partitioning in Hierarchical Trees is used as a method for encoding the transformed GOF. The compression algorithm has been implemented in software, and tested on seven test sequences at different bit-rates. Experimental results indicate a significantly improved performance over MPEG 1 and 2 in terms of picture quality, for sequences filmed with a stationary camera. The codec also performs well on scenes filmed with a moving camera, provided that there is not a large degree of spatial detail present. In addition, the proposed codec has several attractive features. It performs well without entropy coding, and does not require any computationally-expensive motion estimation methods, such as those used by MPEG. Finally, a substantial advantage is that the encoder generates a bit-stream which allows for the progressive transmission of video, making it well-suited to use in video applications over digital networks.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/9697 |
| Date | January 1998 |
| Creators | Servais, Marc Paul |
| Contributors | De Jager, Gerhard |
| Publisher | University of Cape Town, Faculty of Engineering and the Built Environment, Department of Electrical Engineering |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Master Thesis, Masters, MSc |
| Format | application/pdf |
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