PMID- 27849536
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20180730
LR  - 20180730
IS  - 1941-0042 (Electronic)
IS  - 1057-7149 (Linking)
VI  - 26
IP  - 2
DP  - 2017 Feb
TI  - Context Tree-Based Image Contour Coding Using a Geometric Prior.
PG  - 574-589
AB  - Efficient encoding of object contours in images can facilitate advanced 
      image/video compression techniques, such as shape-adaptive transform coding or 
      motion prediction of arbitrarily shaped pixel blocks. We study the problem of 
      lossless and lossy compression of detected contours in images. Specifically, we 
      first convert a detected object contour into a sequence of directional symbols 
      drawn from a small alphabet. To encode the symbol sequence using arithmetic 
      coding, we compute an optimal variable-length context tree (VCT) T via a maximum 
      a posterior (MAP) formulation to estimate symbols' conditional probabilities. MAP 
      can avoid overfitting given a small training set X of past symbol sequences by 
      identifying a VCT T with high likelihood P(X|T) of observing X given T , using a 
      geometric prior P(T) stating that image contours are more often straight than 
      curvy. For the lossy case, we design fast dynamic programming (DP) algorithms 
      that optimally trade off coding rate of an approximate contour [Formula: see 
      text] given a VCT T with two notions of distortion of [Formula: see text] with 
      respect to the original contour x. To reduce the size of the DP tables, a total 
      suffix tree is derived from a given VCT T for compact table entry indexing, 
      reducing complexity. Experimental results show that for lossless contour coding, 
      our proposed algorithm outperforms state-of-the-art context-based schemes 
      consistently for both small and large training datasets. For lossy contour 
      coding, our algorithms outperform comparable schemes in the literature in 
      rate-distortion performance.
FAU - Zheng, Amin
AU  - Zheng A
FAU - Cheung, Gene
AU  - Cheung G
FAU - Florencio, Dinei
AU  - Florencio D
LA  - eng
PT  - Journal Article
DEP - 20161110
PL  - United States
TA  - IEEE Trans Image Process
JT  - IEEE transactions on image processing : a publication of the IEEE Signal 
      Processing Society
JID - 9886191
EDAT- 2016/11/17 06:00
MHDA- 2016/11/17 06:01
CRDT- 2016/11/17 06:00
PHST- 2016/11/17 06:00 [pubmed]
PHST- 2016/11/17 06:01 [medline]
PHST- 2016/11/17 06:00 [entrez]
AID - 10.1109/TIP.2016.2627813 [doi]
PST - ppublish
SO  - IEEE Trans Image Process. 2017 Feb;26(2):574-589. doi: 10.1109/TIP.2016.2627813. 
      Epub 2016 Nov 10.