Divergence Parametric Smoothing in Image Compression Algorithms

The paper elaborates on methods of digital image compression. The focus is on the compression method that represents a raster image as a set of multiply thinned sub-images. Sub-images are processed consecutively to generate special reference images. The difference between the synthesized reference i...

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Bibliographic Details
Published in:Optical memory & neural networks Vol. 33; no. 2; pp. 97 - 101
Main Author: Gashnikov, M. V.
Format: Journal Article
Language:English
Published: Moscow Pleiades Publishing 01.06.2024
Subjects:
Complexity
Computer Science
Information Storage and Retrieval
divergence
digital image
smoothing
bit depth
discreteness
ISSN:1060-992X, 1934-7898
Online Access:Get full text
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Summary:The paper elaborates on methods of digital image compression. The focus is on the compression method that represents a raster image as a set of multiply thinned sub-images. Sub-images are processed consecutively to generate special reference images. The difference between the synthesized reference image and original sub-image forms a divergence array. The algorithm introduces a discrete error into the divergence array to provide the actual bit-depth reduction. However, the introduction of the error inevitably impairs the quality of the decompressed image. The aim is to make sure that the parametric smoothing of divergence arrays can lessen this quality impairment without changing the bit depth reduction originally provided by the method. Numerical experiments on real digital images are carried out to prove that the use of parametric smoothing improves noticeably the efficiency of the image compression method under discussion.
ISSN:1060-992X
1934-7898
DOI:10.3103/S1060992X24700012