Abstract
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.
References
- 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
Abstract
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.
References
- 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.
Details
| Subjects | Engineering |
|---|---|
| Journal Section | Research Articles |
| Authors | |
| Publication Date | June 15, 2017 |
| Acceptance Date | May 15, 2017 |
| Published in Issue | Year 2017 Volume: 18 Issue: 1 |