.png){kind=icon}
返回
Exploiting quantization and spatial correlation in virtual-noise modeling for distributed video coding
.png){kind=icon}
PRE
AI
DOI：10.1016/j.image.2010.05.005.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Aiming for low complexity encoding video coders based on Wyner-Ziv theory are still unsuccessfully trying to match the performance of predictive video coders One of the most important factors concerning the coding performance of distributed coders is modeling and estimating the correlation between the original video signal and its temporal prediction generated at the decoder One of the problems of the state of the art correlation estimators is that their performance is not consistent across a wide range of video content and different coding settings To address this problem we have developed a correlation model able to adapt to changes in the content and the coding parameters by exploiting the spatial correlation of the video signal and the quantization distortion In this paper we describe our model and present experiments showing that our model provides average bit rate gains of up to 12% and average PSNR gains of up to 0 5 dB when compared to the state of the-art models The experiments suggest that the performance of distributed coders can be significantly improved by taking video content and coding parameters into account (c) 2010 Elsevier B V All rights reserved
Keyword:
Context adaptive
Distributed video coding
Virtual noise modeling
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
S
IF:
2.7
论文数:
2.8K
被引数:
4.2K
机构
引用论文
分享收藏Production of Novel Monoclonal Antibodies Against Rabbit Platelet Factor Four新型抗兔血小板因子四单克隆抗体的制备.png)
Hybridoma
HybridomaIF0
PREAI
分享收藏
Identification of Comprehensive Geriatric Assessment Based Risk Factors for Malnutrition in Elderly Asian Cancer PatientsPLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏The complete genome sequence of the Gram-positive bacterium Bacillus subtilis革兰氏阳性细菌枯草芽孢杆菌的全基因组序列NatureOAAI
NatureIF0
OAAI分享收藏 没有更多内容
没有更多内容