Sun’iy intellekt asosida raqamli televideniye signallarida shovqinni real vaqt rejimida aniqlash va yo‘qotishning innovatsion usullari
Annotatsiya
Ushbu maqolada sun’iy intellekt texnologiyalari asosida raqamli televideniye signallarida shovqinni real vaqt rejimida aniqlash va yo‘qotish usullari o‘rganiladi. Zamonaviy raqamli TV tizimlarida signal sifati muhim rol o‘ynaydi, chunki shovqin tasvir va ovoz sifatini sezilarli darajada yomonlashtiradi. Tadqiqotda Digital Video Broadcasting (DVB) signallarini qayta ishlashda Deep Learning va Machine Learning algoritmlarining qo‘llanilishi tahlil qilinadi. Natijalar AI asosidagi yondashuvlar an’anaviy filtrlash usullariga nisbatan yuqori samaradorlikka ega ekanligini ko‘rsatadi.
Kalit so‘zlar: sun’iy intellekt, raqamli TV, DVB, shovqinni kamaytirish, real vaqt tizimi, neyron tarmoqlar, signalni qayta ishlash
Библиографические ссылки
1. Haykin, S. (2019). Neural Networks and Learning Machines. Pearson Education.
2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
3. Proakis, J. G., & Manolakis, D. G. (2007). Digital Signal Processing: Principles, Algorithms and Applications. Pearson.
4. Oppenheim, A. V., & Schafer, R. W. (2010). Discrete-Time Signal Processing. Prentice Hall.
5. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
6. ITU-R BT.601. (2011). Studio encoding parameters of digital television. International Telecommunication Union.
7. DVB Project Office. (2020). Digital Video Broadcasting Standards Overview. DVB.
8. Wang, Z., Bovik, A. C. (2009). “Mean Squared Error: Love It or Leave It?” IEEE Signal Processing Magazine.
9. Zhang, K., Zuo, W., & Zhang, L. (2018). “FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising.” IEEE Transactions on Image Processing.
10. IEEE Transactions on Broadcasting. (2021). Recent Advances in AI-based Video Signal Processing.
11. Gonzalez, R. C., & Woods, R. E. (2018). Digital Image Processing. Pearson.
12. Goodfellow, I. (2020). Deep Learning Applications in Signal Processing. MIT Lecture Notes.
