Generalized statistics framework for rate distortion theory

Venkatesan RC; Plastino A

首页> 外文期刊>Physica, A. Statistical mechanics and its applications >Generalized statistics framework for rate distortion theory

【24h】

Generalized statistics framework for rate distortion theory

机译：率失真理论的广义统计框架

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

Variational principles for the rate distortion (RD) theory in lossy compression are formulated within the ambit of the generalized nonextensive statistics of Tsallis, for values of the nonextensivity parameter satisfying 0 < q < 1 and q > 1. Alternating minimization numerical schemes to evaluate the nonextensive RD function, are derived. Numerical simulations demonstrate the efficacy of generalized statistics RD models.

机译：在Tsallis的广义非扩展统计量的范围内，针对非扩展性参数的值满足0 1，制定了有损压缩率失真（RD）理论的变分原理。派生非扩展的RD函数。数值模拟证明了广义统计RD模型的有效性。

著录项

来源
《Physica, A. Statistical mechanics and its applications》 |2009年第12期|共17页
作者
Venkatesan RC; Plastino A;
展开▼
作者单位

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类物理学;
关键词
Generalized Tsallis statistics; Rate distortion; Additive duality; Compression information; Nonextensive alternating minimization; algorithm; Distortion-compression plane;

机译：广义Tsallis统计率失真附加对偶压缩信息无张力交替最小化算法压缩压缩平面;

相似文献

外文文献
中文文献
专利

1. Generalized statistics framework for rate distortion theory [J] . Venkatesan RC, Plastino A Physica, A. Statistical mechanics and its applications . 2009,第12期

机译：率失真理论的广义统计框架
2. Event-driven mathematical framework for noisy integrate-and-fire neuron networks: spike trains statistics via stochastic calculus, network analysis inspired by queuing theory and an event-driven simulator [J] . Jonathan Touboul, Olivier Faugeras, Olivier Rochel BMC Neuroscience . 2007,第SUPPLEMENTa2期

机译：噪声激发并集成的神经元网络的事件驱动数学框架：通过随机演算，峰值训练统计信息，受排队论启发的网络分析和事件驱动模拟器
3. Event-driven mathematical framework for noisy integrate-and-fire neuron networks: spike trains statistics via stochastic calculus, network analysis inspired by queuing theory and an event-driven simulator [J] . Jonathan Touboul, Olivier Faugeras, Olivier Rochel BMC Neuroscience . 2007,第SUPPLEMENTa2期

机译：噪声激发并集成的神经元网络的事件驱动数学框架：通过随机演算，峰值训练统计信息，受排队论启发的网络分析和事件驱动模拟器
4. From rapid distortion theory to statistical closure theories of anisotropic turbulence [C] . Claude Cambon Theories of Turbulence Held at the International Centre For Mechanical Sciences . 2002

机译：从快速扭曲理论到各向异性湍流的统计闭合理论
5. Generalized higher-order statistics (GHOS) and generalized higher-order cyclic statistics (GHOCS): Theory and applications. [D] . Gurelli, Mehmet Izzet. 1997

机译：广义高阶统计量（GHOS）和广义高阶循环统计量（GHOCS）：理论与应用。
6. Event-driven mathematical framework for noisy integrate-and-fire neuron networks: spike trains statistics via stochastic calculus network analysis inspired by queuing theory and an event-driven simulator [O] . Jonathan Touboul, Olivier Faugeras, Olivier Rochel 2007

机译：噪声激发并集成的神经元网络的事件驱动数学框架：通过随机演算训练峰值统计信息受排队论启发的网络分析和事件驱动模拟器
7. Generalized Statistics Framework for Rate Distortion Theory [O] . R. C. Venkatesan A, A. Plastino B 2009

机译：速率失真理论的广义统计框架

Generalized statistics framework for rate distortion theory

摘要

著录项

相似文献

相关主题

期刊订阅