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

• SUMI Toru

  Tokyo University of Science

• INAMURA Yuta

  Tokyo University of Science

• KAMEDA Yusuke

  Tokyo University of Science

• ISHIKAWA Tomokazu

  Tokyo University of Science

• MATSUDA Ichiro

  Tokyo University of Science

• ITOH Susumu

  Tokyo University of Science

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.

Journal

• IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

  IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E100.A (11), 2351-2354, 2017

  The Institute of Electronics, Information and Communication Engineers

Keywords

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

Details

• CRID: 1390001206311782912
• NII Article ID: 130006191411
• DOI: 10.1587/transfun.e100.a.2351
• ISSN: 17451337
• ISSN: 09168508
• Web Site: https://www.jstage.jst.go.jp/article/transfun/E100.A/11/E100.A_2351/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed