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  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E100-A   No.11   pp.2351-2354
Publication Date: 2017/11/01
Online ISSN: 1745-1337
Type of Manuscript: Special Section LETTER (Special Section on Smart Multimedia & Communication Systems)
Category: Image Processing
Keyword: 
lossless coding,  still-image coding,  template matching,  probability modeling,  linear prediction,  

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Summary: 
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.