JOURNAL ARTICLE

Binary shape coding using baseline-based method

Shi Hwa LeeDae-Sung ChoYu-Shin ChoSe Hoon SonEuee S. JangJae-Seob ShinYang Seock Seo

Year: 1999 Journal:   IEEE Transactions on Circuits and Systems for Video Technology Vol: 9 (1)Pages: 44-58   Publisher: Institute of Electrical and Electronics Engineers

Abstract

Here, we propose a new shape-coding algorithm called baseline-based binary shape coding (BBSC), where the outer or inner contours of an arbitrarily shaped object are represented by traced one-dimensional (l-D) data from the baseline with turning points (TP's). There are two coding modes, i.e., the intra and inter modes as in texture coding. In the intra mode, the differential values of the neighboring 1-D distance values and TP's corresponding to the given shape are encoded by the entropy coder. In the inter mode, object identification, global shape matching and local contour matching are employed for motion compensation/estimation. Lossy shape coding is enabled by variable sampling in each contour segment or by allowing some predefined error when performing motion compensation. We compare the proposed method with the bitmap-based method of context-based arithmetic encoding (CAE). Simulation results show that the proposed method is better than CAE in coding efficiency for intra mode and better in subjective quality for both intra and inter modes, although the CAE method has performed better than the proposed method in inter mode.

Keywords:
Computer science Baseline (sea) Binary number Coding (social sciences) Artificial intelligence Mathematics Arithmetic Statistics

Metrics

22
Cited By
2.61
FWCI (Field Weighted Citation Impact)
10
Refs
0.90
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Image Retrieval and Classification Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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