.png){kind=icon}
返回
Variable-length constrained-storage tree-structured vector quantization
.png){kind=icon}
PRE
AI
DOI：10.1109/83.748888.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Constrained storage vector quantization, (CSVQ), introduced by Chan anti Gersho [2]-[4], allows for the stagewise design of balanced tree-structured residual vector quantization codebooks with low encoding and storage complexities, On the other hand, it has been established in [9], [11], and [12] that variable-length tree-structured vector quantizer (VLTSVQ) yields better coding performance than a balanced tree-structured vector quantizer and mag even outperform a full-search vector quantizer due to the nonuniform distribution of rate among the subsets of its input space, The variable-length constrained storage tree-structured vector quantization (VLCS-TSVQ) algorithm presented in this paper utilizes the codebook sharing by multiple vector sources concept as in CSVQ to greedily grow an unbalanced tree structured residual vector quantizer with constrained storage. It is demonstrated by simulations on test sets from various synthetic one-dimensional (I-D) sources and real-world images that the performance of VLCS-TSVQ, whose codebook storage complexity varies linearly with rate, can come very close to the performance of greedy growth VLTSVQ of [11] and [12], The dramatically reduced size of the overall codebook allows the transmission of the codevector probabilities as side information for source adaptive entropy coding.
Keyword:
adaptive coding
tree data structures
vector quantization
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
暂无机构信息
引用论文
Wildfire activity and land use drove 20th‐century changes in forest cover in the Colorado front range.png)
Ecosphere
OAAI
EcosphereIF0
OAAI分享收藏分享收藏
查看更多内容