Parametric estimation of structural similarity degradation for video transmission over error-prone networks

Young Jae Kwon; Jong Seok Lee

doi:10.1049/el.2013.1951

Parametric estimation of structural similarity degradation for video transmission over error-prone networks

Young Jae Kwon, Jong Seok Lee

Research output: Contribution to journal › Article › peer-review

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Abstract

A parametric model to estimate the degradation of objective video quality over error-prone networks is proposed. The model estimates an expected quality degradation in terms of one of the most reliable perceptual quality metrics, structural similarities (SSIMs), for a given encoded video and network condition described by a packet loss rate. The simulation results demonstrate that the proposed model can estimate the expected SSIM degradation of H.264/ advanced video coding encoded videos with high accuracy.

Original language	English
Pages (from-to)	1147-1148
Number of pages	2
Journal	Electronics Letters
Volume	49
Issue number	18
DOIs	https://doi.org/10.1049/el.2013.1951
Publication status	Published - 2013 Aug 29

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

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10.1049/el.2013.1951

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title = "Parametric estimation of structural similarity degradation for video transmission over error-prone networks",

abstract = "A parametric model to estimate the degradation of objective video quality over error-prone networks is proposed. The model estimates an expected quality degradation in terms of one of the most reliable perceptual quality metrics, structural similarities (SSIMs), for a given encoded video and network condition described by a packet loss rate. The simulation results demonstrate that the proposed model can estimate the expected SSIM degradation of H.264/ advanced video coding encoded videos with high accuracy.",

author = "Kwon, {Young Jae} and Lee, {Jong Seok}",

year = "2013",

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AB - A parametric model to estimate the degradation of objective video quality over error-prone networks is proposed. The model estimates an expected quality degradation in terms of one of the most reliable perceptual quality metrics, structural similarities (SSIMs), for a given encoded video and network condition described by a packet loss rate. The simulation results demonstrate that the proposed model can estimate the expected SSIM degradation of H.264/ advanced video coding encoded videos with high accuracy.

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Parametric estimation of structural similarity degradation for video transmission over error-prone networks

Abstract

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