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Compression of signs of DCT coefficients for additional lossless compression of JPEG images

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publicationElectronic Imaging
Subtitle of host publicationImage Processing: Algorithms and Systems XVI
PublisherSociety for Imaging Science and Technology
Publication statusPublished - 2018
Publication typeA4 Article in a conference publication
EventIS&T International Symposium on Electronic Imaging -
Duration: 28 Jan 20182 Feb 2018

Publication series

ISSN (Electronic)2470-1173


ConferenceIS&T International Symposium on Electronic Imaging


One of the main approaches to additional lossless compression of JPEG images is decoding of quantized values of discrete cosine transform (DCT) coefficients and further more effective recompression of the coefficients. Values of amplitudes of DCT coefficients are highly correlated and it is possible to effectively compress them. At the same time, signs of DCT coefficients, which occupy up to 20% of compressed image, are often considered unpredictable. In the paper, a new and effective method for compression of signs of quantized DCT coefficients is proposed. The proposed method takes into account both correlation between DCT coefficients of the same block and correlation between DCT coefficients of neighbor blocks. For each of 64 DCT coefficients, positions of 3 reference coefficients inside the block are determined and stored in the compressed file. Four reference coefficients with fixed positions are used from the neighbor blocks. For all reference coefficients, 15 frequency models to predict signs of a given coefficient are used. All 7 probabilities (that the sign is negative) are mixed by logistic mixing. For test set of JPEG images, we show that the proposed method allows compressing signs of DCT coefficients by 1.1 ⋯ 1.3 times, significantly outperforming nearest analogues.


  • Discrete cosine transform, JPEG, JPEG additional compression, Sign compression

Publication forum classification

Field of science, Statistics Finland