Abstract
Veri saklama teknolojilerindeki hızlı gelismelere rağmen goruntu verisinin saklanması ve/veya tasınması onemini yitirmemistir. Goruntunun kodlanması ve sıkıstırılması konusundaki calısmalar yoğun bir sekilde devam etmektedir. Yapılan calısmalar daha fazla sıkıstırma oranı ve daha iyi bir goruntu kalitesini yakalamak icindir. Video goruntu dizilerinin kodlanması icin bircok teknik gelistirilmistir. Bu calısmada, imgeler uzerinde basarılı sonuclar veren vektor nicemleme yontemi imge dizilerinin kodlanmasında kullanılmıstır. Cok bilinen vektor nicemleme algoritmalarından Linde-Buzo-Gray algoritması ozyinelemeli bir surec olduğundan buyuk islem gucu gerektirmektedir. Bu calısmada, cerceveler arasındaki yüksek iliskiyi kullanarak her bir cerceve icin yeni bir kod-kitabı olusturmak yerine ilk cerceveden olusturulan kod-kitabını sonraki cerceveler icin guncelleyen bir algoritma onerilmistir. Guncelleme algoritması sayesinde yaklasık %1 bozulmaya karsılık zamandan 3.5:1 oranında kazanc elde edilmistir.
References
- [1] Umbaugh, S. E., Computer Vision and Image Processing, Prentice-Hall International Inc., 1998.
- [2] Gonzalez, R., Woods R., Digital Image Processing, Addision-Wesley Publishing Company, 1992.
- [3] Duda R. O., Hart P. E. ve Stork D.G., Pattern Classification 2nd Edition, Wiley-Interscience Publication, USA.2001.
- [4] Y. Linde, A. Buzo, & R. M. Gray, “ An Algorithm for Vector Quantization Design ,” IEEE Transactions on Communication, v. COM-208, pp.84-95, 1980.
- [5] A. Gersho, R. M. Gray, Vector Quantization and Signal Compression, Kluwer Academic, 1992.
- [6] Gray R., Vector Quantization, IEEE ASSP Magazin, April, 1984.
- [7] Barlaud M., Pyramadial Lattice Vector Quantization for Multiscale Image Coding., IEEE Trans. on Image Processing, Vol.3, No.4, July, 1994.
- [8] Nasrabadi N., Feng Y., Image Compression Using Address-Vector Quantization, IEEE Trans. on Communications, Vol.38, No.12, December, 1980.
- [9] Nasrabadi N., King R., Image Coding Using Vector Quantization: A Review, IEEE Trans. on Communications, Vol 36., No.8, August, 1998.
- [10] Nasrabadi N., Feng Y., Image Compression Using Address-Vector Quantization, IEEE Trans. on Communications, Vol.38, No.12, December, 1980.
- [11] K. Özkan, E. Seke, LBG Algoritmasının Görüntü Dizeleri Đçin Güncellenmesi, Elektrik-Elektronik ve Bilgisayar Sempozyumu, ELECO 2006, 6 –10 Aralık 2006, Bursa
- [12] K. Özkan, LBG Algoritmasında Görüntü Đstatistiklerinden Faydalanma, Y. Lisans Tezi, Osmangazi Üniversitesi, Eskisehir, Temmuz 2000
Abstract
Despite rapid progress in mass-storage density, processor speeds, and digital communication system performance, demand for data storage capacity and data transmission bandwidth continues to outstrip the capabilities of available technologies. Studies on image coding and compression techniques are still going on. The objective is to achieve higher compression ratios with minimum loss of quality. Many video coding techniques have been developed. Vector quantization (VQ) is being used successfully for still images and we used VQ for coding video sequences. One of the well-known VQ algorithms is the popular Linde-Buzo-Gray algorithm. Since the LBG algorithm is an iterative process, its complexity for video sequences is very high that it requires extraordinary computational power. In this paper, we propose an algorithm that progressively updates the VQ codebooks taking advantage of high correlation between consecutive frames instead of generating a new codebook for each frame or group of frames. We have experimentally shown that with the proposed technique computational gains of 3.5:1 can be achieved at the expense of 1% distortion.
References
- [1] Umbaugh, S. E., Computer Vision and Image Processing, Prentice-Hall International Inc., 1998.
- [2] Gonzalez, R., Woods R., Digital Image Processing, Addision-Wesley Publishing Company, 1992.
- [3] Duda R. O., Hart P. E. ve Stork D.G., Pattern Classification 2nd Edition, Wiley-Interscience Publication, USA.2001.
- [4] Y. Linde, A. Buzo, & R. M. Gray, “ An Algorithm for Vector Quantization Design ,” IEEE Transactions on Communication, v. COM-208, pp.84-95, 1980.
- [5] A. Gersho, R. M. Gray, Vector Quantization and Signal Compression, Kluwer Academic, 1992.
- [6] Gray R., Vector Quantization, IEEE ASSP Magazin, April, 1984.
- [7] Barlaud M., Pyramadial Lattice Vector Quantization for Multiscale Image Coding., IEEE Trans. on Image Processing, Vol.3, No.4, July, 1994.
- [8] Nasrabadi N., Feng Y., Image Compression Using Address-Vector Quantization, IEEE Trans. on Communications, Vol.38, No.12, December, 1980.
- [9] Nasrabadi N., King R., Image Coding Using Vector Quantization: A Review, IEEE Trans. on Communications, Vol 36., No.8, August, 1998.
- [10] Nasrabadi N., Feng Y., Image Compression Using Address-Vector Quantization, IEEE Trans. on Communications, Vol.38, No.12, December, 1980.
- [11] K. Özkan, E. Seke, LBG Algoritmasının Görüntü Dizeleri Đçin Güncellenmesi, Elektrik-Elektronik ve Bilgisayar Sempozyumu, ELECO 2006, 6 –10 Aralık 2006, Bursa
- [12] K. Özkan, LBG Algoritmasında Görüntü Đstatistiklerinden Faydalanma, Y. Lisans Tezi, Osmangazi Üniversitesi, Eskisehir, Temmuz 2000
Details
| Subjects | Electrical Engineering |
|---|---|
| Journal Section | Research Articles |
| Authors | |
| Publication Date | June 30, 2008 |
| Acceptance Date | December 24, 2007 |
| Published in Issue | Year 2008 Volume: 21 Issue: 1 |
