SavedAdd your journal

Journals / Trakya Üniversitesi Fen Bilimleri Dergisi / 2010 / Cilt: 11 - Sayı: 1

IMAGE CODING BY KLT METHOD

KLT YÖNTEMİ ile İMGE KODLAMA

Rafet AKDENİZ
Pages
1–6
Publication Date
2016-08-05
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It has been shown that, short frame length performs better than long frame length in image data compression.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this work, images which have different frame length are modeled by KLT (Karhunen Loeve Transform).
📊
Key Findings & Data:PSNR is used as the performance criteria.
💡
Conclusion & Impact:It has been shown that, short frame length performs better than long frame length in image data compression.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:İmge Kodlama · KLT · İmge sıkştırma

Abstract

In this work, images which have different frame length are modeled by KLT (Karhunen Loeve Transform). PSNR is used as the performance criteria. It has been shown that, short frame length performs better than long frame length in image data compression.

Özet

Bu çalışmada çokluortamda veri sıkıştırma amacıyla yoğun olarak kullanılan KLT (Karhunen Loewe Dönüşümü) yöntemi ayrıntılı bir biçimde incelenmiş, farklı imge ve farklı çerçeve uzunlukları için kodlama gerçekleştirilmiştir. PSNR ile sonuçlar değerlendirilmiş ve çerçeve uzunlukları arttıkça kodlama başarımının düştüğü tesbit edilmiştir.

🔖

Cite This Article

AKDENİZ, R. (2016). IMAGE CODING BY KLT METHOD. Trakya Üniversitesi Fen Bilimleri Dergisi, Cilt: 11 - Sayı: 1, 1–6. https://ajindex.com/articles/722623

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  1. Sayoud K., Introduction to Data Compressing, 3rd Ed., Morgan Kaufmann Series, ISBN: 012620862X, 2005.
  2. Moinuddin, A.A., Khan E., Ghanbari M., ”Efficient algorithm for very low bit rate embedded image coding”, IET Image Process, Vol.2, pp.59-71, 2008.
  3. Gopi E.S., Algorithm Collections for Digital Signal Processing Applications using MATLAB, Springer, ISBN: 1- 4020-6409-8, 2007.
  4. Jayant N.S., Noll P., Digital Coding of Waveforms, Prentice-Hall, NJ, 1984.
  5. Akdeniz, R., A.M. Karas, B.S Yarman, Turkish Speech Coding by Signature Base Sequences, Proc. ICSPATInternational Conference on Signal Processing App. & Technology, Toronto, Canada, pp.1291-1294, 1998.
  6. Akdeniz, R., B.S. Yarman, Generation of Optimum Signature Base Sequences for Speech Signals, Proc.ICASSP2000
  7. International Conference on Acoustic, Speech and Signal Processing, Proc. CD No:3945,Istanbul,Turkey, June 2000.
  8. Akdeniz, R., Tüfekçi P., Yarman B.S., “Determination of Optimum Length of Frames and Number of Vectors to Code ECG Signals”, International Journal of Computational Intelligence, p:58-61, ISSN: 1(2) 1304-4508, 2003.
  9. Akdeniz R., Yarman B.S., “New Statistical Method to Represent Speech Signals.” Asian Journal of Information Technology, p:721-726, ISSN: 3(9) 1682-3915, 2004.
  10. Akdeniz R., Yarman B.S., “A Novel Method to Represent Speech Signals.” Signal Processing (EURASIP), p:37-50, ISSN: 85(1) 0165-1684, 2005.
  11. Bourlard H., Konig Y., Morgan N., A training algorithm for statistical sequence recognition with applications to transition-based speech recognition, IEEE Signal Processing Letters, 3 (7), July 1996.
  12. Noll P., Speech and Audio Coding for Multimedia Communications, Proc. Internat.Cost 254 Workshop on Intelligent Comm. technologies and Applications., Neuchatel,Schweiz, 1999.