Title
Adaptive Neural-Network-Based Lossless Image Coder with Preprocessed Input Data
Authors
[ 1 ] Instytut Telekomunikacji Multimedialnej, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee
Scientific discipline (Law 2.0)
Year of publication
2025
Published in
Article type
scientific article
Publication language
english
Keywords
EN
- Artificial Neural Network
- image coding
- lossless coding
Abstract
EN It is shown in this paper that the appropriate preprocessing of input data may result in an important reduction of Artificial Neural Network (ANN) training time and simplification of its structure, while improving its performance. The ANN is working as a data predictor in a lossless image coder. Its adaptation is done for each coded pixel separately; no initial training using learning image sets is necessary. This means that there is no extra off-line time needed for initial ANN training, and there are no problems with network overfitting. There are two concepts covered in this paper: Replacement of image pixels by their differences diminishes data variability and increases ANN convergence (Concept 1); Preceding ANN by advanced predictors reduces ANN complexity (Concept 2). The obtained codecs are much faster than one without modifications, while their data compaction properties are clearly better. It outperforms the JPEG-LS codec by approximately 10%.
Pages (from - to)
2603-1 - 2603-12
Comments
Article number: 2603
License type
CC BY (attribution alone)
Open Access Mode
open journal
Open Access Text Version
final published version
Full text of article
Access level to full text
public
Ministry points / journal
100
Impact Factor
2,5 [List 2023]
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