Evidence map›Paper›PMID 39609503›Full record

ArticleScientific reports2024

A case study on entropy-aware block-based linear transforms for lossless image compression.

Borut Žalik, David Podgorelec, Ivana Kolingerová, Damjan Strnad, Štefan Kohek

Abstract read
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In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Borut ŽalikFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000, Maribor, Slovenia. borut.zalik@um.si.
David PodgorelecFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000, Maribor, Slovenia.
Ivana KolingerováDepartment of Computer Science and Engineering, University of West Bohemia, Technická 8, 306 14, Plzen̆, Czech Republic.
Damjan StrnadFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000, Maribor, Slovenia.
Štefan KohekFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000, Maribor, Slovenia.

Funding

Grantová Agentura České Republiky (Grant Agency of the Czech Republic) 23-04622LThe Slovenian Research and Innovation Agency (ARIS) J2-4458The Slovenian Research and Innovation Agency (ARIS) P2-0041
6 · The paper itself

Abstract

Data compression algorithms tend to reduce information entropy, which is crucial, especially in the case of images, as they are data intensive. In this regard, lossless image data compression is especially challenging. Many popular lossless compression methods incorporate predictions and various types of pixel transformations, in order to reduce the information entropy of an image. In this paper, a block optimisation programming framework

Indexed as

Computer scienceInformation entropyInverse distance transformPredictionString transformations

Identifiers

PMID39609503
PMCPMC11605056

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.