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Erschienen in:
Digital Image Watermarking Using Sine Transform Technique Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Prediction-Based Lossless Image Compression

verfasst von : Mohamed Uvaze Ahamed Ayoobkhan, Eswaran Chikkannan, Kannan Ramakrishnan, Saravana Balaji Balasubramanian

Erschienen in: Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB)

Verlag: Springer International Publishing

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Abstract

In this paper, a lossless image compression technique using prediction errors is proposed. To achieve better compression performance, a novel classifier which makes use of wavelet and Fourier descriptor features is employed. Artificial neural network (ANN) is used as a predictor. An optimum ANN configuration is determined for each class of the images. In the second stage, an entropy encoding is performed on the prediction errors which improve the compression performance further. The prediction process is made lossless by making the predicted values as integers both at the compression and decompression stages. The proposed method is tested using three types of datasets, namely CLEF med 2009, COREL1 k and standard benchmarking images. It is found that the proposed method yields good compression ratio values in all these cases and for standard images, the compression ratio values achieved are higher compared to those obtained by the known algorithms.

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Vorheriges Kapitel Fractional Reaction Diffusion Model for Parkinson’s Disease
Nächstes Kapitel Audio and Video Streaming in Telepresence Application Using WebRTC for Healthcare System
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Metadaten
Titel
Prediction-Based Lossless Image Compression
verfasst von
Mohamed Uvaze Ahamed Ayoobkhan
Eswaran Chikkannan
Kannan Ramakrishnan
Saravana Balaji Balasubramanian
Copyright-Jahr
2019
Buch
Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB)

Print ISBN: 978-3-030-00664-8

Electronic ISBN: 978-3-030-00665-5

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-00665-5_161

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