UPPER BOUND ON SNR GAIN IN WAVELET TRANSFORM PREDICTIVE-ENTROPY IMAGE-CODING

Using the rate-distortion theory approach and assuming the Laplacian p robability density function of the quantizer input signal, we propose a function which gives the signal-to-quantizing-noise ratio (SNR) gain in discrete wavelet transform predictive-entropy coding over fullband predictive image coding. The upper bound on SNR gain is determined as a function of the subband number only. The practical SNR performances of realizable wavelet still image coders are compared with their theo retical bounds. The computer simulation results of wavelet based predi ctive coded test images show that the SNR gain grows faster with the s ubband number increment than its theoretical upper bounds do.