.png){kind=icon}
返回
Context Tree-Based Image Contour Coding Using a Geometric Prior
.png){kind=icon}
OA
AI
DOI：10.1109/TIP.2016.2627813.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 vertical bar 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 (x) over cap given a VCT T with two notions of distortion of (x) over cap 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.
Keyword:
Contour coding
lossless coding
statistical learning
image compression
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容