Name
Abstract | ||
|---|---|---|
The paper deals with encoding the contours of given regions in an image. All contours are represented as a sequence of contour segments, each such segment being defined by an anchor (starting) point and a string of contour edges, equivalent to a string of chain-code symbols. We propose efficient ways for anchor points selection and contour segments generation by analyzing contour crossing points and imposing rules that help in minimizing the number of anchor points and in obtaining chain-code contour sequences with skewed symbol distribution. When possible, part of the anchor points are efficiently encoded relative to the currently available contour segments at the decoder. The remaining anchor points are represented as ones in a sparse binary matrix. Context tree coding is used for all entities to be encoded. The results for depth map compression are similar (in lossless case) or better (in lossy case) than the existing results. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIP.2014.7026138 | Image Processing |
Keywords | Field | DocType |
data compression,decoding,image coding,image segmentation,image sequences,sparse matrices,anchor points coding,anchor points selection,chain-code contour sequences,chain-code symbol string,context tree coding,contour crossing point analysis,contour encoding,contour segment sequence,contour segments generation,decoder,depth map compression,skewed symbol distribution,sparse binary matrix,Lossless and lossy compression,anchor points,contour compression,depth map | Computer vision,Compression (physics),Logical matrix,Pattern recognition,Lossy compression,Computer science,Coding (social sciences),Artificial intelligence,Depth map,Data compression,Encoding (memory),Lossless compression | Conference |
ISSN | Citations | PageRank |
1522-4880 | 3 | 0.42 |
References | Authors | |
17 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| I. Schiopu | 1 | 37 | 8.04 |
| Ioan Tabus | 2 | 276 | 38.23 |