For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
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
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
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
lossless coding, still-image coding, template matching, probability modeling, linear prediction,
Full Text: PDF(470.2KB)
>>Buy this Article
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.