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Cited by 631

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Video coding with optimal inter/intra-mode switching for packet loss resilience

Zhang, R., Regunathan, S.L., Rose, K.·2000·IEEE Journal on Selected Areas in Communications

Abstract

Resilience to packet loss is a critical requirement in predictive video coding for transmission over packet-switched networks, since the prediction loop propagates errors and causes substantial degradation in video quality. This work proposes an algorithm to optimally estimate the overall distortion of decoder frame reconstruction due to quantization, error propagation, and error concealment. The method recursively computes the total decoder distortion at pixel level precision to accurately account for spatial and temporal error propagation. The accuracy of the estimate is demonstrated via simulation results. The estimate is integrated into a rate-distortion (RD)-based framework for optimal switching between intra-coding and inter-coding modes per macroblock. The cost in computational complexity is modest. The framework is further extended to optimally exploit feedback/acknowledgment information from the receiver/network. Simulation results both with and without a feedback channel demonstrate that precise distortion estimation enables the coder to achieve substantial and consistent gains in PSNR over known state-of-the-art RD- and non-RD-based mode switching methods.

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Metadata

Authors
Zhang, R., Regunathan, S. L., & Rose, K.
Venue
IEEE Journal on Selected Areas in Communications
Published
2000-06
Volume
18
Issue
6
Pages
966-976
ISSN
0733-8716 (ISSN-L)
OpenAlex
W2156498695
Cited by
631
References
8
FWCI
33.58
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Video coding with optimal inter/intra-mode switching for packet loss resilience. https://app.cochat.ai/papers/10.1109/49.848250
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Zhang, R., Regunathan, S. L., & Rose, K. (2000). Video coding with optimal inter/intra-mode switching for packet loss resilience. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/49.848250

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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