Pregled bibliografske jedinice broj: 91508
An Algorithm for a Fast Estimation of the Wavelet Subbands Statistics
An Algorithm for a Fast Estimation of the Wavelet Subbands Statistics // Proceedings of the 3rd EURASIP Conference focused on Digital Signal Processing for Multimedia Communications and Services, ECMCS'2001 / Kalman Fazekas (ur.).
Budimpešta: Scientific Association of Infocommunications - HTE, 2001. str. 284-287 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 91508 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
An Algorithm for a Fast Estimation of the Wavelet Subbands Statistics
Autori
Rimac-Drlje, Snježana ; Zovko-Cihlar, Branka ; Grgić, Sonja
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 3rd EURASIP Conference focused on Digital Signal Processing for Multimedia Communications and Services, ECMCS'2001
/ Kalman Fazekas - Budimpešta : Scientific Association of Infocommunications - HTE, 2001, 284-287
Skup
3rd EURASIP Conference focused on Digital Signal Processing for Multimedia Communications and Services, ECMCS'2001
Mjesto i datum
Budimpešta, Mađarska, 11.09.2001. - 13.09.2001
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Wavelet Transform; Image Compression; Probability Density Function; Quantization Noise
Sažetak
Discrete wavelet transform - DWT is successfuly used for image compression in different coders. The knowledge of DWT coefficients statistics can improve behavior of the coding techniques. The paper proposes a fast algorithm for estimation of the wavelet coefficients probability density function - PDF. Wavelet subband PDF is approximated well by generalized Gaussian function, but parameter k of that function widely differs for different subbands, and it also differs for different images. The parameter k determines the shape of the PDF and it influences compression ratio and quantization noise power in the image compression procedure. The proposed algorithm is based on the quantization noise power dependance on the parameter k. We show the usage of the estimated PDF for calculation of the mass center of quantization intervals, which we used as reconstruction levels. So, we obtained an improvement in PSNR (Peak Signal-to-Noise Ratio) of reconstructed images between 0.25 and 0.4 dB.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb