Compression through extraction of learned parameters from images in de-correlated image space

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021832" target="_blank" >RIV/62690094:18450/24:50021832 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s42044-024-00173-0" target="_blank" >https://link.springer.com/article/10.1007/s42044-024-00173-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s42044-024-00173-0" target="_blank" >10.1007/s42044-024-00173-0</a>

Result language

angličtina

Original language name

Compression through extraction of learned parameters from images in de-correlated image space

Original language description

Image compression is a class of algorithms that reduces the storage space requirement for a digital image. Lossy image compression techniques achieve higher compression but the visual quality of the decompressed image is degraded many times. Decompressed images lose their visual appeal due to compression artifacts. These compression artifacts are introduced due to the quantization step of the compression phase. We developed a lossy image compression technique that works on the spatial domain and de-correlated color model. For the luminance channel compression, the modified Vector Quantization method is used. In the case of chrominance channels, a feature vector is built for each pixel using the neighborhood statistics and cluster information of the pixel. For all the pixels of the image, using these feature vectors, a training dataset is formed. For the training of an artificial neural network (ANN), a feature vector of a pixel is used as the input and its respective chrominance value is used as the target output. Two training datasets are used to train two ANNs separately—one for the Cb channel and one for the Cr channel. These two trained ANNs are stored as the compressed form for the chrominance channels. During the decompression process, first, the luminance channel is reconstructed. Later, for each chrominance channel, the respective trained ANN predicts the chrominance values for each pixel. Thus, the whole image is reconstructed. The method has been tested on the benchmark images and also color images from the UCID v.2 database. The experimental result shows that the method successfully avoids the blocking artifacts in the reconstructed images. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Iran Journal of Computer Science

ISSN

2520-8438

e-ISSN

2520-8446

Volume of the periodical

7

Issue of the periodical within the volume

2

Country of publishing house

CH - SWITZERLAND

Number of pages

19

Pages from-to

259-277

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85207825927