&lt;title&gt;Centering of context-dependent components of prediction-error distributions of images&lt;/title&gt;

Langdon, Glen G.; Mareboyana, Manohar

doi:10.1117/12.158637

Cited by 15 publications

(7 citation statements)

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“…According to this distribution, the probability of an integer value of the prediction error is proportional to , where controls the two-sided exponential decay rate. However, it was observed in [15] that a dc offset is typically present in context-conditioned prediction error signals. This offset is due to integer-value constraints and possible bias in the prediction step.…”

Section: ) Parameterization: Tsgd Modelmentioning

confidence: 99%

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The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS

Weinberger

Seroussi

Sapiro

2000

IEEE Trans. on Image Process.

1,368

732

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Abstract-LOCO-I (LOw COmplexity LOssless COmpression for Images) is the algorithm at the core of the new ISO/ITU standard for lossless and near-lossless compression of continuous-tone images, JPEG-LS. It is conceived as a "low complexity projection" of the universal context modeling paradigm, matching its modeling unit to a simple coding unit. By combining simplicity with the compression potential of context models, the algorithm "enjoys the best of both worlds." It is based on a simple fixed context model, which approaches the capability of the more complex universal techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with an extended family of Golomb-type codes, which are adaptively chosen, and an embedded alphabet extension for coding of low-entropy image regions. LOCO-I attains compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. Moreover, it is within a few percentage points of the best available compression ratios, at a much lower complexity level. We discuss the principles underlying the design of LOCO-I, and its standardization into JPEG-LS.

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Section: ) Parameterization: Tsgd Modelmentioning

confidence: 99%

“…under 128). Mechanisms for "centering" image prediction error distributions, based on integer offset models, are described in [15] and [17].…”

Section: Bias Computation In Jpeg-lsmentioning

confidence: 99%

The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS

Weinberger

Seroussi

Sapiro

2000

IEEE Trans. on Image Process.

1,368

732

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“…While UCM provided the best published compression results at the time (at the cost of high complexity), it could be argued that the improvement over the fixed model structure paradigm, best represented by the Sunset family of algorithms (further developed in [21] and [22]), was scant. However, the image modeling principles outlined in [53] shed light on the workings of some of the leading lossless image compression algorithms.…”

Section: Modeling For High Performance Compression With Arithmetimentioning

confidence: 99%

Lossless compression of continuous-tone images

2000

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“…Sun8xb incorporates ideas from JPEG lossless for coding extrabits. Further work toward CaTH was performed at the CESDIS facilty of the NASA Goddard Space Flight Center in the summers of 1992 and 1993, and was reported in [9][10].…”

Section: Introductionmentioning

confidence: 99%

<title>Context-dependent distribution shaping and parameterization for lossless image compression</title>

Langdon

Haidinyak

1994

SPIE Proceedings

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An algorithm class called CaTH (Centering and Tail Handling) is described that is based on predictive coding followed by adaptive binary arithmetic coding. CaTH treats the prediction errors close to zero (i.e., near the center of the error distribution) in a more precise manner than the errors of the "tails" (i.e., errors far from zero). The context model uses error buckets (quantized ranges) of prediction errors. The probability model for the prediction errors uses a histogram for the center. A variety of ways to binarize the tails are studied. The results on the suite of JPEG test images are very encouraging.

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<title>Centering of context-dependent components of prediction-error distributions of images</title>

Cited by 15 publications

References 4 publications

The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS

The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS

Lossless compression of continuous-tone images

<title>Context-dependent distribution shaping and parameterization for lossless image compression</title>

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