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Context modeling for near-lossless image coding

delete2002-03-01
delete32
PRE
AI
B
Bruno Aiazzi
L
Luciano Alparone
S
Stefano Baronti
DOI：10.1109/97.995822delete
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Abstract

Abstract

En 中文
This letter describes a context-based entropy coding suitable for any causal spatial differential pulse code modulation (DPCM) scheme performing lossless or near-lossless image coding. The proposed method is based on partitioning of prediction errors into homogeneous classes before arithmetic coding. A context function is measured on prediction errors lying within a two-dimensional (2-D) causal neighborhood, comprising the prediction support of the current pixel, as the root mean square (RMS) of residuals weighted by the reciprocal of their Euclidean distances. Its effectiveness is demonstrated in comparative experiments concerning both lossless and near-lossless coding. The proposed context coding/decoding is strictly real-time.
Keywords:
arithmetic coding
DPCM
entropy coding
near-lossless coding
statistical context modeling
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

IEEE Signal Processing Magazine cover
IEEE Signal Processing Magazine
IF:
9.6
Papers:
1.1W
Citations:
1.7W

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