Evaluation and performance analysis of Chinese remainder theorem and its application to lossless image compression
Chinese remainder theorem (CRT) is widely utilized in many cryptographic applications and additionally the reversible nature of CRT is employed in compression of images. This paper mainly focuses on the suitability of CRT for lossless image compression and the analysis is carried out for the number...
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| Published in | Journal of ambient intelligence and humanized computing Vol. 14; no. 6; pp. 6645 - 6660 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2023
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1868-5137 1868-5145 |
| DOI | 10.1007/s12652-021-03532-y |
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| Abstract | Chinese remainder theorem (CRT) is widely utilized in many cryptographic applications and additionally the reversible nature of CRT is employed in compression of images. This paper mainly focuses on the suitability of CRT for lossless image compression and the analysis is carried out for the number and range of primes to be chosen. With respect to the analysis is carried out for the number of primes to be chosen (i.e., 2, 3, 4, 5, and 6), it is found that CRT suits well only for the chosen number of primes 2 with good compression ratio. For the remaining prime numbers, it provides negligible or even negative CR based on the chosen number of prime numbers. Also, CRT based lossless compression (CRTLC) reduces the size of the image based on the number of primes chosen. Further, it can achieve substantial compression of the original image. Using different test images, CRT is compared with recent lossless compression methods and against the standard set of lossless compression techniques (i.e., JPEG 2000, JPEG-LS, and CALIC). From these comparisons, it is inferred that CRT scores (maximum achieved CR is 1.8823) better than the recent and standard algorithms. |
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| AbstractList | Chinese remainder theorem (CRT) is widely utilized in many cryptographic applications and additionally the reversible nature of CRT is employed in compression of images. This paper mainly focuses on the suitability of CRT for lossless image compression and the analysis is carried out for the number and range of primes to be chosen. With respect to the analysis is carried out for the number of primes to be chosen (i.e., 2, 3, 4, 5, and 6), it is found that CRT suits well only for the chosen number of primes 2 with good compression ratio. For the remaining prime numbers, it provides negligible or even negative CR based on the chosen number of prime numbers. Also, CRT based lossless compression (CRTLC) reduces the size of the image based on the number of primes chosen. Further, it can achieve substantial compression of the original image. Using different test images, CRT is compared with recent lossless compression methods and against the standard set of lossless compression techniques (i.e., JPEG 2000, JPEG-LS, and CALIC). From these comparisons, it is inferred that CRT scores (maximum achieved CR is 1.8823) better than the recent and standard algorithms. |
| Author | Brindha, M. Vidhya, R. |
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| Keywords | Lossless compression Compression ratio CRT Bit rate |
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Cohn, eds, Citeseer, pp 351–360 BrindhaMGoundenNAA chaos based image encryption and lossless compression algorithm using hash table and Chinese remainder theoremAppl Soft Comput20164037939010.1016/j.asoc.2015.09.055 VenugopalDMohanSRajaSAn efficient block based lossless compression of medical imagesOptik2016127275475810.1016/j.ijleo.2015.10.154 Ghobaei-AraniMSouriALP-WSC: a linear programming approach for web service composition in geographically distributed cloud environmentsJ Supercomput20197552603262810.1007/s11227-018-2656-3 ShenHPanWDWuDPredictive lossless compression of regions of interest in hyperspectral images with no-data regionsIEEE Trans Geosci Remote Sens201655117318210.1109/TGRS.2016.2603527 KalluriMJiangMLingNZhengJZhangPAdaptive RD optimal sparse coding with quantization for image compressionIEEE Trans Multimed2018211395010.1109/TMM.2018.2847228 KuoHCLinYLA hybrid algorithm for effective lossless compression of video display framesIEEE Trans Multimed201214350050910.1109/TMM.2012.2191945 KhelifiFBouridaneAKurugolluFJoined spectral trees for scalable SPIHT-based multispectral image compressionIEEE Trans Multimed200810331632910.1109/TMM.2008.917357 ZhuSLiMChenCLiuSZengBCross-space distortion directed color image compressionIEEE Trans Multimed201720352553810.1109/TMM.2017.2749162 SECTOR S, ITU O (1998) Information technology–lossless and near-lossless compression of continuous-tone still images–baseline KimSChoNIHierarchical prediction and context adaptive coding for lossless color image compressionIEEE Trans Image Process2013231445449326201810.1109/TIP.2013.22934281374.94176 Jagannathan V, Mahadevan A, Hariharan R, Srinivasan E (2007) Number theory based image compression encryption and application to image multiplexing. 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| References_xml | – reference: WeinbergerMJSenior member, IEEE, Gadiel Seroussi, fellow, IEEE, and Guillermo Sapiro, member, IEEE, the LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS in image processingIEEE Trans2000913091324 – reference: Ghobaei-AraniMSouriABakerTHussienAControcity: an autonomous approach for controlling elasticity using buffer management in cloud computing environmentIEEE Access2019710691210692410.1109/ACCESS.2019.2932462 – reference: JTC I (2000) Information technology-JPEG2000 image coding system part 1: core coding system. ISO/IEC 15444-1 – reference: Wang P, Dai R, Akyildiz IF (2010) Collaborative data compression using clustered source coding for wireless multimedia sensor networks. In: 2010 Proceedings IEEE INFOCOM, IEEE, pp 1–9 – reference: YangFMouJSunKChuRLossless image compression-encryption algorithm based on bp neural network and chaotic systemMultimed Tools Appl20207927199631999210.1007/s11042-020-08821-w – reference: Ammar A, Al Kabbany A, Youssef M, Amam A (2001) A secure image coding scheme using residue number system. In: Proceedings of the eighteenth national radio science conference, NRSC’2001 (IEEE Cat. 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In: 2020 3rd International conference on information and communications technology (ICOIACT), IEEE, pp 170–175 – reference: KuoHCLinYLA hybrid algorithm for effective lossless compression of video display framesIEEE Trans Multimed201214350050910.1109/TMM.2012.2191945 – reference: LiSXuMRenYWangZClosed-form optimization on saliency-guided image compression for HEVC-MSPIEEE Trans Multimed201720115517010.1109/TMM.2017.2721544 – reference: ZhangYAdjerohDAPrediction by partial approximate matching for lossless image compressionIEEE Trans Image Process2008176924935251661410.1109/TIP.2008.920772 – reference: McClellen JH, Rader CM (1979) Number theory in digital signal processing. Prentice Hall Professional Technical Reference – reference: Ghobaei-AraniMRahmanianAAAslanpourMSDashtiSECSA-WSC: cuckoo search algorithm for web service composition in cloud environmentsSoft Comput201822248353837810.1007/s00500-017-2783-4 – reference: ZhangHXqWangYjSunXyWangA novel method for lossless image compression and encryption based on LWT, SPIHT and cellular automataSignal Process Image Commun202084115829 – reference: ZhuSLiMChenCLiuSZengBCross-space distortion directed color image compressionIEEE Trans Multimed201720352553810.1109/TMM.2017.2749162 – reference: Howard PG, Vitter J (1994) Fast and e cient lossless image compression. In: Proc. data compression conference, JA Storer and M. Cohn, eds, Citeseer, pp 351–360 – reference: PeiDSalomaaADingCChinese remainder theorem: applications in computing, coding, cryptography1996SingaporeWorld Scientific0907.11002 – reference: Wang W, Swamy M, Ahmad MO (2004) RNS application for digital image processing. In: 4th IEEE international workshop on system-on-chip for real-time applications, IEEE, pp 77–80 – reference: WiegandTSchwarzHSource coding: part I of fundamentals of source and video coding2011NorwellNow Publishers Inc1213.94001 – reference: David T, Marcellin M (2012) Jpeg2000 image compression fundamentals, standards and practice: image compression fundamentals, standards and practice, vol 642. Springer Science & Business Media – reference: ChenYZhaoXZhangLKangJWMultiview and 3d video compression using neighboring block based disparity vectorsIEEE Trans Multimed201618457658910.1109/TMM.2016.2525010 – reference: KasbanHHashimaSAdaptive radiographic image compression technique using hierarchical vector quantization and Huffman encodingJ Ambient Intell Humaniz Comput20191072855286710.1007/s12652-018-1016-8 – reference: LucasLFRodriguesNMda Silva CruzLAde FariaSMLossless compression of medical images using 3-D predictorsIEEE Trans Med Imaging201736112250226010.1109/TMI.2017.2714640 – reference: ShenHPanWDWuDPredictive lossless compression of regions of interest in hyperspectral images with no-data regionsIEEE Trans Geosci Remote Sens201655117318210.1109/TGRS.2016.2603527 – reference: KalluriMJiangMLingNZhengJZhangPAdaptive RD optimal sparse coding with quantization for image compressionIEEE Trans Multimed2018211395010.1109/TMM.2018.2847228 – reference: Wu X, Memon N (1996) CALIC-a context based adaptive lossless image codec. In: 1996 IEEE international conference on acoustics, speech, and signal processing conference proceedings, IEEE, vol 4, pp 1890–1893 – reference: Jagannathan V, Mahadevan A, Hariharan R, Srinivasan E (2007) Number theory based image compression encryption and application to image multiplexing. 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| SubjectTerms | Accuracy Algorithms Artificial Intelligence Compression ratio Computational Intelligence Cryptography Data compression Data encryption Engineering Image compression Medical research Methods Multimedia Original Research Prime numbers Robotics and Automation Theorems User Interfaces and Human Computer Interaction Video compression |
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| Title | Evaluation and performance analysis of Chinese remainder theorem and its application to lossless image compression |
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