Image classification for jpeg compression

• Autorzy: Tichonov Jevgenij , Kurasova Olga , Filatovas Ernestas

Identyfikatory

DOI

10.12913/22998624/87041

• Języki publikacji: EN

Abstrakty

EN

We analyse storage problems of digital images in accordance with image quality and image compression efficiency. Storage problems are relevant for Cloud storage and file hosting services, online file storage providers, social networks, etc. In this paper, an approach is proposed to process a group of images with a JPEG algorithm that all the processed images satisfy the minimum threshold of quality with the automatic selection of the quality factor (QF). The experimental investigation reveals advantages of the compression efficiency of the proposed approach over the traditional JPEG algorithm. The proposed approach enables saving storage spaces while maintaining the desirable image quality.

Słowa kluczowe

EN

image quality; image classification; JPEG algorithm; storage of images; quality prediction; SSIM; PSNR

PL

jakość obrazu; klasyfikacja obrazu; algorytm JPEG; przechowywanie obrazów; prognozowanie jakości; SSIM; PSNR

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2018
• Tom: Vol. 12, no 2
• Strony: 29--34
• Opis fizyczny: Bibliogr. 18 poz., fig., tab.

Twórcy

autor

Tichonov Jevgenij

• Vilnius University, Institute of Mathematics and Informatics, Akademijos str. 4, LT-08663, Vilnius

autor

Kurasova Olga

• Vilnius University, Institute of Mathematics and Informatics, Akademijos str. 4, LT-08663, Vilnius

autor

Filatovas Ernestas

• Vilnius Gediminas Technical University, Sauletekio av. 11, LT-10223, Vilnius

Bibliografia

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• 10. Pigeon, S. and Coulombe, S., Computationally efficient algorithms for predicting the file size of JPEG images subject to changes of quality factor and scaling. Proc. 24th Queen’s Biennial Symp. Kingston, Canada, Jun., 2008, 378–382.
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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).