Lossless Image Coding Based on Probability Modeling Using Template Matching and Linear Prediction

Toru SUMI; Yuta INAMURA; Yusuke KAMEDA; Tomokazu ISHIKAWA; Ichiro MATSUDA; Susumu ITOH

doi:10.1587/transfun.E100.A.2351

Lossless Image Coding Based on Probability Modeling Using Template Matching and Linear Prediction

Toru SUMI, Yuta INAMURA, Yusuke KAMEDA, Tomokazu ISHIKAWA, Ichiro MATSUDA, Susumu ITOH

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We previously proposed a lossless image coding scheme using example-based probability modeling, wherein the probability density function of image signals was dynamically modeled pel-by-pel. To appropriately estimate the peak positions of the probability model, several examples, i.e., sets of pels whose neighborhoods are similar to the local texture of the target pel to be encoded, were collected from the already encoded causal area via template matching. This scheme primarily makes use of non-local information in image signals. In this study, we introduce a prediction technique into the probability modeling to offer a better trade-off between the local and non-local information in the image signals.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.11 pp.2351-2354

Publication Date: 2017/11/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E100.A.2351

Type of Manuscript: Special Section LETTER (Special Section on Smart Multimedia & Communication Systems)

Category: Image Processing

Authors

Toru SUMI
  Tokyo University of Science
Yuta INAMURA
  Tokyo University of Science
Yusuke KAMEDA
  Tokyo University of Science
Tomokazu ISHIKAWA
  Tokyo University of Science
Ichiro MATSUDA
  Tokyo University of Science
Susumu ITOH
  Tokyo University of Science

Keyword

lossless coding, still-image coding, template matching, probability modeling, linear prediction

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Toru SUMI, Yuta INAMURA, Yusuke KAMEDA, Tomokazu ISHIKAWA, Ichiro MATSUDA, Susumu ITOH, "Lossless Image Coding Based on Probability Modeling Using Template Matching and Linear Prediction" in IEICE TRANSACTIONS on Fundamentals, vol. E100-A, no. 11, pp. 2351-2354, November 2017, doi: 10.1587/transfun.E100.A.2351.
Abstract: We previously proposed a lossless image coding scheme using example-based probability modeling, wherein the probability density function of image signals was dynamically modeled pel-by-pel. To appropriately estimate the peak positions of the probability model, several examples, i.e., sets of pels whose neighborhoods are similar to the local texture of the target pel to be encoded, were collected from the already encoded causal area via template matching. This scheme primarily makes use of non-local information in image signals. In this study, we introduce a prediction technique into the probability modeling to offer a better trade-off between the local and non-local information in the image signals.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.2351/_p

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@ARTICLE{e100-a_11_2351,
author={Toru SUMI, Yuta INAMURA, Yusuke KAMEDA, Tomokazu ISHIKAWA, Ichiro MATSUDA, Susumu ITOH, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Lossless Image Coding Based on Probability Modeling Using Template Matching and Linear Prediction},
year={2017},
volume={E100-A},
number={11},
pages={2351-2354},
abstract={We previously proposed a lossless image coding scheme using example-based probability modeling, wherein the probability density function of image signals was dynamically modeled pel-by-pel. To appropriately estimate the peak positions of the probability model, several examples, i.e., sets of pels whose neighborhoods are similar to the local texture of the target pel to be encoded, were collected from the already encoded causal area via template matching. This scheme primarily makes use of non-local information in image signals. In this study, we introduce a prediction technique into the probability modeling to offer a better trade-off between the local and non-local information in the image signals.},
keywords={},
doi={10.1587/transfun.E100.A.2351},
ISSN={1745-1337},
month={November},}

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TY - JOUR
TI - Lossless Image Coding Based on Probability Modeling Using Template Matching and Linear Prediction
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2351
EP - 2354
AU - Toru SUMI
AU - Yuta INAMURA
AU - Yusuke KAMEDA
AU - Tomokazu ISHIKAWA
AU - Ichiro MATSUDA
AU - Susumu ITOH
PY - 2017
DO - 10.1587/transfun.E100.A.2351
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2017
AB - We previously proposed a lossless image coding scheme using example-based probability modeling, wherein the probability density function of image signals was dynamically modeled pel-by-pel. To appropriately estimate the peak positions of the probability model, several examples, i.e., sets of pels whose neighborhoods are similar to the local texture of the target pel to be encoded, were collected from the already encoded causal area via template matching. This scheme primarily makes use of non-local information in image signals. In this study, we introduce a prediction technique into the probability modeling to offer a better trade-off between the local and non-local information in the image signals.
ER -