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Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
Telecommunications and Radio Engineering
SJR: 0.203 SNIP: 0.44 CiteScore™: 1

ISSN Печать: 0040-2508
ISSN Онлайн: 1943-6009

Выпуски:
Том 79, 2020 Том 78, 2019 Том 77, 2018 Том 76, 2017 Том 75, 2016 Том 74, 2015 Том 73, 2014 Том 72, 2013 Том 71, 2012 Том 70, 2011 Том 69, 2010 Том 68, 2009 Том 67, 2008 Том 66, 2007 Том 65, 2006 Том 64, 2005 Том 63, 2005 Том 62, 2004 Том 61, 2004 Том 60, 2003 Том 59, 2003 Том 58, 2002 Том 57, 2002 Том 56, 2001 Том 55, 2001 Том 54, 2000 Том 53, 1999 Том 52, 1998 Том 51, 1997

Telecommunications and Radio Engineering

DOI: 10.1615/TelecomRadEng.v77.i17.40
pages 1535-1554

SMART LOSSY COMPRESSION OF IMAGES BASED ON DISTORTION PREDICTION

S. S. Krivenko
National Aerospace University (Kharkiv Aviation Institute), 17 Chkalov St., Kharkiv, 61070, Ukraine
O. Krylova
Kharkiv National Medical University, 4 Nauka Ave., Kharkiv, 61022, Ukraine
E. Bataeva
Kharkiv University of Humanities "People's Ukrainian Academy", 27 Lermontovskaya St., Kharkiv, 61024, Ukraine
V. V. Lukin
National Aerospace University (Kharkiv Aviation Institute), 17 Chkalov St., Kharkiv, 61070, Ukraine

Краткое описание

Images of different origin are used nowadays in numerous applications spreading the tendency of world digitalization. Despite increase of memory of computers and other electronic carriers of information, amount of memory needed for saving and managing digital data (images and video in the first order) increases faster making crucial the task of their efficient compression. Efficiency means not only appropriate compression ratio but also appropriate speed of compression and quality of compressed images. In this paper, we analyze how this can be reached for coders based on discrete cosine transform (DCT). The novelty of our approach consists in fast and simple analysis of DCT coefficient statistics in a limited number of 8×8 pixel blocks with further rather accurate prediction of mean square error (MSE) of introduced distortions for a given quantization step. Then, a proper quantization step can be set with ensuring the condition that MSE of introduced errors is not greater than a preset value to provide a desired quality. In this way, multiple compressions/decompressions are avoided and the desired quality is provided quickly and with appropriate accuracy. We present examples of applying the proposed approach.

Ключевые слова: lossy compression, image, quality, efficiency

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