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Cited by 458

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

Unequal loss protection: graceful degradation of image quality over packet erasure channels through forward error correction

Mohr, A.E., Riskin, E.A., Ladner, R.E.·2000·IEEE Journal on Selected Areas in Communications

Abstract

We present the unequal loss protection (ULP) framework in which unequal amounts of forward error correction are applied to progressive data to provide graceful degradation of image quality as packet losses increase. We develop a simple algorithm that can find a good assignment within the ULP framework. We use the set partitioning in hierarchical trees coder in this work, but our algorithm can protect any progressive compression scheme. In addition, we promote the use of a PMF of expected channel conditions so that our system can work with almost any model or estimate of packet losses. We find that when optimizing for an exponential packet loss model with a mean loss rate of 20% and using a total rate of 0.2 bits per pixel on the Lenna image, good image quality can be obtained even when 40% of transmitted packets are lost.

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Metadata

Authors
Mohr, A. E., Riskin, E. A., & Ladner, R. E.
Venue
IEEE Journal on Selected Areas in Communications
Published
2000-06
Volume
18
Issue
6
Pages
819-828
ISSN
0733-8716 (ISSN-L)
OpenAlex
W2117492831
Cited by
458
References
24
FWCI
28.49
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Unequal loss protection: graceful degradation of image quality over packet erasure channels through forward error correction. https://app.cochat.ai/papers/10.1109/49.848236
The paper
Mohr, A. E., Riskin, E. A., & Ladner, R. E. (2000). Unequal loss protection: graceful degradation of image quality over packet erasure channels through forward error correction. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/49.848236

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.