TAILIEUCHUNG - Báo cáo hóa học: " Hybrid 3D Fractal Coding with Neighbourhood Vector Quantisation"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Hybrid 3D Fractal Coding with Neighbourhood Vector Quantisation | EURASIP Journal on Applied Signal Processing 2004 16 2571-2579 2004 Hindawi Publishing Corporation Hybrid 3D Fractal Coding with Neighbourhood Vector Quantisation Zhen Yao Computer Science Department University of Warwick Coventry CV4 7AL UK Email yao@ Roland Wilson Computer Science Department University of Warwick Coventry CV4 7AL UK Email rgw@ Received 31 August 2003 Revised 12 September 2004 A hybrid 3D compression scheme which combines fractal coding with neighbourhood vector quantisation for video and volume data is reported. While fractal coding exploits the redundancy present in different scales neighbourhood vector quantisation as a generalisation of translational motion compensation is a useful method for removing both intra- and interframe coherences. The hybrid coder outperforms most of the fractal coders published to date while the algorithm complexity is kept relatively low. Keywords and phrases fractal compression video coding neighbourhood vector quantisation convergence. 1. INTRODUCTION Fractal image compression techniques introduced by Barnsley and Jacquin 1 2 are the product of the study of iterated function systems IFS . These techniques involve an approach to compression quite different from standard transform coder-based methods. Transform coders model images in a simple fashion as vectors drawn from a wide-sense stationary random process and store images as quantized transform coefficients. Fractal block coders assume that image redundancy can be efficiently exploited through self-similarity on a blockwise basis. They represent images by contraction maps of which the images are approximate fixed points. Images are decoded by iterating these maps to their fixed points. The fundamental principle of fractal coding is to represent the image by a set of contractive mappings. First the image I is partitioned into a set of blocks R r1 r2 . rn that covers I referred to as the range blocks. For each r e R a domain .

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