Araştırma Makalesi
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ENTROPY BASED ESTIMATION ALGORITHM USING SPLIT IMAGES TO INCREASE COMPRESSION RATIO

Yıl 2017, Cilt 18, Sayı 1, 31 - 41, 15.06.2017

Öz

Compressing image files after splitting them into certain number of parts can increase compression ratio. Acquired compression ratio can also be increased by compressing each part of the image using different algorithms, because each algorithm gives different compression ratios on different complexity values. In this study, statistical compression results and measured complexity values of split images are obtained, and an estimation algorithm based on these results is presented. Our algorithm splits images into 16 parts, compresses each part with different algorithm and joins the images after compression. Compression results show that using our estimation algorithm acquires higher compression ratios over whole image compression techniques with ratio of 5% on average and 25% on maximum.

Kaynakça

  • 1. CCITT Rec. T.81, (ISO/IEC 10918-1, 1994), Digital Compression and Coding of Continuous-Tone Still Images – Requirements And Guidelines (JPEG), 1992. 2. ISO/IEC 15444-1, JPEG 2000 image coding system: Core coding system. 2004. 3. CHRISTOPOULOS C, SKODRAS A, EBRAHIMI T. The JPEG2000 still image coding system: An Overview. IEEE Transactions on Consumer Electronics, 46(4), 1103-1127, 2000. 4. ITU-T Rec. T.832, (ISO/IEC 29199-2, 2010), JPEG XR image coding system: Image coding specification. 2009. 5. ISO/IEC 15948:2004, Information technology -- Computer graphics and image processing -- Portable Network Graphics (PNG): Functional specification, 2004. 6. AHMED N, NATARAJAN T, RAO KR. Discrete Cosine Transform. IEEE Transactions on Computers, 23(1): 90-93, 1974. 7. MALLAT S. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Pattern Analysis and Machine Intelligence. 11(7), 674-693, 1989. 8. DEUTSCH LP. DEFLATE Compressed Data Format Specification version 1.3. IETF Network Working Group, Request for Comments 1951, 1996. 9. CLEARY J, WITTEN I. Data Compression Using Adaptive Coding and Partial String Matching, IEEE Transactions on Communications. 32 (4): 396–402, 1984. 10. SHKARIN D. PPM: One Step to Practicality, Proceedings of IEEE Data Compression Conference. 202-211, 2002, Utah, USA. 11. BURROWS M, WHEELER DJ. A block sorting lossless data compression algorithm, Technical Report 124, Digital Equipment Corporation. 1994. 12. ÖZTÜRK E. Transformation and Segmentation Operations on Images for Increasing the Effectiveness of Compression Methods, MSc Thesis, Trakya University, 2012. 13. FELDSPAR A. An explanation of the deflate algorithm. http://www.zlib.net/feldspar.html. Retrieved 2017-02-13. 14. ÖZTÜRK E, MESUT A. Finding the Optimal Lossless Compression Method for Images Using Machine Learning Algorithms, International Scientific Conference UNITECH’16, II,345-348 Gabrovo-Bulgaria, 2016. 15. ZIV J, LEMPEL A. A Universal Algorithm for Sequential Data Compression. IEEE Transactions on Information Theory, Vol. 23, 1977, pp. 337-343. 16. ZIV J, LEMPEL A. Compression of Individual Sequences via Variable-Rate Coding. IEEE Transactions on Information Theory, Vol. 24, 1978, pp. 530-536. 17. WELCH TA. A Technique for High-Performance Data Compression. IEEE Computer, Vol. 17, 1984, No. 6, pp. 8-19. 18. STORER JA, Szymanski TG. Data compression via textual substitution. Journal of the ACM, Vol. 29, 1982, pp. 928-951. 19. Wikipedia. Lempel–Ziv–Markov chain algorithm. http://en.wikipedia.org/wiki/LZMA. Retrieved 2017-02-13. 20. Gelbmann M. The PNG image file format is now more popular than GIF. W3Techs. Q-Success. 2013-01-31.

SIKIŞTIRMA ORANINI ARTTIRMAK İÇİN BÖLÜNMÜŞ RESİMLERİ KULLANAN ENTROPİ TABANLI BIR TAHMİN ALGORİTMASI

Yıl 2017, Cilt 18, Sayı 1, 31 - 41, 15.06.2017

Öz

Resim dosyalarını belirli sayıda parçalara böldükten sonra sıkıştırma işlemi yapmak sıkıştırma oranını arttırabilmektedir. Elde edilen sıkıştırma oranı resmin her parçasının farklı bir algoritmayla sıkıştırılması ile daha da fazla arttırılabilmektedir. Her algoritma farklı karmaşıklık değerlerinde farklı sıkıştırma oranı sağlamaktadır. Bu çalışmada bölünmüş resimlerden sıkıştırma sonuçları istatistikleri ve ölçülen karmaşıklık değerleri elde edilmiş ve bu sonuçları kullanan bir tahmin algoritması önerilmiştir. Algoritmamız resimleri 16 parçaya böler, her parçayı farklı bir algoritmayla sıkıştırır ve bu parçaları en son aşamada birleştirir. Sıkıştırma sonuçlarından görüldüğü üzere algoritmamız resmi tek parça halinde sıkıştırma işlemine göre ortalama %5 ve maksimum %25 daha iyi sıkıştırma performansı sağlamıştır.

Kaynakça

  • 1. CCITT Rec. T.81, (ISO/IEC 10918-1, 1994), Digital Compression and Coding of Continuous-Tone Still Images – Requirements And Guidelines (JPEG), 1992. 2. ISO/IEC 15444-1, JPEG 2000 image coding system: Core coding system. 2004. 3. CHRISTOPOULOS C, SKODRAS A, EBRAHIMI T. The JPEG2000 still image coding system: An Overview. IEEE Transactions on Consumer Electronics, 46(4), 1103-1127, 2000. 4. ITU-T Rec. T.832, (ISO/IEC 29199-2, 2010), JPEG XR image coding system: Image coding specification. 2009. 5. ISO/IEC 15948:2004, Information technology -- Computer graphics and image processing -- Portable Network Graphics (PNG): Functional specification, 2004. 6. AHMED N, NATARAJAN T, RAO KR. Discrete Cosine Transform. IEEE Transactions on Computers, 23(1): 90-93, 1974. 7. MALLAT S. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Pattern Analysis and Machine Intelligence. 11(7), 674-693, 1989. 8. DEUTSCH LP. DEFLATE Compressed Data Format Specification version 1.3. IETF Network Working Group, Request for Comments 1951, 1996. 9. CLEARY J, WITTEN I. Data Compression Using Adaptive Coding and Partial String Matching, IEEE Transactions on Communications. 32 (4): 396–402, 1984. 10. SHKARIN D. PPM: One Step to Practicality, Proceedings of IEEE Data Compression Conference. 202-211, 2002, Utah, USA. 11. BURROWS M, WHEELER DJ. A block sorting lossless data compression algorithm, Technical Report 124, Digital Equipment Corporation. 1994. 12. ÖZTÜRK E. Transformation and Segmentation Operations on Images for Increasing the Effectiveness of Compression Methods, MSc Thesis, Trakya University, 2012. 13. FELDSPAR A. An explanation of the deflate algorithm. http://www.zlib.net/feldspar.html. Retrieved 2017-02-13. 14. ÖZTÜRK E, MESUT A. Finding the Optimal Lossless Compression Method for Images Using Machine Learning Algorithms, International Scientific Conference UNITECH’16, II,345-348 Gabrovo-Bulgaria, 2016. 15. ZIV J, LEMPEL A. A Universal Algorithm for Sequential Data Compression. IEEE Transactions on Information Theory, Vol. 23, 1977, pp. 337-343. 16. ZIV J, LEMPEL A. Compression of Individual Sequences via Variable-Rate Coding. IEEE Transactions on Information Theory, Vol. 24, 1978, pp. 530-536. 17. WELCH TA. A Technique for High-Performance Data Compression. IEEE Computer, Vol. 17, 1984, No. 6, pp. 8-19. 18. STORER JA, Szymanski TG. Data compression via textual substitution. Journal of the ACM, Vol. 29, 1982, pp. 928-951. 19. Wikipedia. Lempel–Ziv–Markov chain algorithm. http://en.wikipedia.org/wiki/LZMA. Retrieved 2017-02-13. 20. Gelbmann M. The PNG image file format is now more popular than GIF. W3Techs. Q-Success. 2013-01-31.

Ayrıntılar

Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Emir Öztürk> (Sorumlu Yazar)
Trakya University
0000-0002-3734-5171


Altan Mesut>
Trakya University
0000-0002-1477-3093

Yayımlanma Tarihi 15 Haziran 2017
Yayınlandığı Sayı Yıl 2017, Cilt 18, Sayı 1

Kaynak Göster

IEEE E. Öztürk ve A. Mesut , "SIKIŞTIRMA ORANINI ARTTIRMAK İÇİN BÖLÜNMÜŞ RESİMLERİ KULLANAN ENTROPİ TABANLI BIR TAHMİN ALGORİTMASI", Trakya Üniversitesi Mühendislik Bilimleri Dergisi, c. 18, sayı. 1, ss. 31-41, Haz. 2017