.png){kind=icon}
返回
Conditional entropy-constrained trellis-coded RVQ with application to image coding
.png){kind=icon}
PRE
AI
DOI：10.1109/97.823522.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This letter introduces an extension of conditional entropy-constrained RVQ (CEC-RVQ) that embodies trellis-coded quantization. The method, which we call conditional entropy-constrained trellis-coded residual vector quantization (CEC-TCRVQ), quantizes a supervector (made from a large number of neighboring vectors) to better extract the two-dimensional (2-D) correlation present in real images. Simulation results indicate that CEC-TCRVQ provides 0.3-0.4 dB improvement over CEC-RVQ for the 4 x 4 vector case and 1.3 dB improvement for the 8 x 8 case.
Keyword:
entropy-constrained
image compression
multistage VQ
trellis-coding
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:
9.6
论文数:
1.1W
被引数:
1.7W
机构
暂无机构信息
.png){kind=icon}
没有更多内容