Optimal single pass SNR scalable video coder

Lisimachos P. Kondi; Aggelos K Katsaggelos

Optimal single pass SNR scalable video coder

Lisimachos P. Kondi^*, Aggelos K Katsaggelos

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Scopus citations

Abstract

In this paper, we introduce a new methodology for SNR video scalability which is based on the partitioning of the DCT coefficients. The partitioning is done in a way that is optimal in the Rate-Distortion sense. The optimization is performed using Lagrangian relaxation and Dynamic Programming (DP). Experimental results are presented and conclusions are drawn.

Original language	English (US)
Title of host publication	IEEE International Conference on Image Processing
Publisher	IEEE
Pages	276-280
Number of pages	5
Volume	1
State	Published - Dec 1 1999
Event	International Conference on Image Processing (ICIP'99) - Kobe, Jpn Duration: Oct 24 1999 → Oct 28 1999

Other

Other	International Conference on Image Processing (ICIP'99)
City	Kobe, Jpn
Period	10/24/99 → 10/28/99

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Hardware and Architecture
Electrical and Electronic Engineering

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title = "Optimal single pass SNR scalable video coder",

abstract = "In this paper, we introduce a new methodology for SNR video scalability which is based on the partitioning of the DCT coefficients. The partitioning is done in a way that is optimal in the Rate-Distortion sense. The optimization is performed using Lagrangian relaxation and Dynamic Programming (DP). Experimental results are presented and conclusions are drawn.",

author = "Kondi, {Lisimachos P.} and Katsaggelos, {Aggelos K}",

year = "1999",

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AU - Katsaggelos, Aggelos K

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AB - In this paper, we introduce a new methodology for SNR video scalability which is based on the partitioning of the DCT coefficients. The partitioning is done in a way that is optimal in the Rate-Distortion sense. The optimization is performed using Lagrangian relaxation and Dynamic Programming (DP). Experimental results are presented and conclusions are drawn.

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M3 - Conference contribution

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