Title :
Shape approximation through recursive scalable layer generation
Author :
Melnikov, Gennady ; Katsaggelos, Aggelos K.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Northwestern Univ., Evanston, IL, USA
Abstract :
This paper presents an efficient recursive algorithm for generating operationally optimal intra mode scalable layer decompositions of object contours. The problem is posed in terms of minimizing the shape distortion at full reconstruction subject to the total (for all scalable layers) bit budget constraint. Based on the chosen vertex-based representation, we solve the problem of determining the number and locations of approximating vertices for all scalable layers jointly and optimally. The number of scalable layers is not constrained, but, rather, is a by-product of the proposed optimization. The algorithm employs two different coding strategies: one for the base layer and one for the enhancement layers. By carefully defining scalable layer recursion and base layer segment costs the problem is solved by executing a directed acyclic graph (DAG) shortest path algorithm.
Keywords :
approximation theory; data compression; directed graphs; image coding; image representation; optimisation; rate distortion theory; approximating vertices; base layer coding; bit budget constraint; directed acyclic graph; efficient recursive algorithm; enhancement layers; image coding; image reconstruction; object contours; optimal intra-mode scalable layer decompositions; optimization; rate distortion; recursive scalable layer generation; shape approximation; shape distortion minimisation; shortest path algorithm; vertex-based representation; Constraint optimization; Costs; Decoding; Encoding; Image databases; Image segmentation; Information retrieval; MPEG 7 Standard; Rate-distortion; Shape;
Conference_Titel :
Image Processing, 2000. Proceedings. 2000 International Conference on
Conference_Location :
Vancouver, BC, Canada
Print_ISBN :
0-7803-6297-7
DOI :
10.1109/ICIP.2000.899864