TAILIEUCHUNG - emerging wireless multimedia services and technologies phần 2

suy nghĩ của một quảng cáo 5-10 từ hiệu quả chỉ đến một trang mà đáp ứng yêu cầu của quảng cáo. Nếu quảng cáo của bạn đọc Nikon D100 Lời khuyên: Làm thế nào để nhận được nhiều nhất của máy ảnh của bạn, bạn không muốn nó dẫn đến một trang về việc làm cho tách cà phê hoàn hảo! | 26 Multimedia Coding Techniques for Wireless Networks Clearly the original sequence can be reproduced from the initial sample x 0 and the sequence d n by recursively using x n x n 1 d n for n 1 . The idea behind coding sequence d n instead of x n is that usually d n is less correlated and thus according to the observation of Section it assumes lower entropy. Indeed assuming without loss of generality that E x n 0 autocorrelation rd m of d n can be calculated as follows rd m E d n d n m E x n x n 1 x n m x n m 1 E x n x n m E x n 1 x n m 1 E x n x n m 1 E x n 1 x n m 2rx m - rx m - 1 - rx m 1 0 where in the last row of we used the assumption that the autocorrelation coefficient rx m is very close to the average of rx m 1 and rx m 1 . In view of Equation we may expect that under certain conditions not always though the correlation between successive samples of d n is low even in the case where the original sequence x n is highly correlated. We thus expect that d n has lower entropy than x n . In practice the whole procedure is slightly more complicated because d n should be quantized as well. This means that the decoder cannot use Equation as it would result in the accumulation of a quantization error. For this reason the couple of expressions are replaced by d n x n x n 1 where x n d n x n 1 d n QQ d n . DPCM as already described is essentially a one-step ahead prediction procedure namely x n 1 is used as a prediction of x n and the prediction error is next coded. This procedure can be generalized and enhanced if the prediction takes into account more past samples weighted appropriately in order to capture the signal s statistics. In this case Equations and are replaced by their generalized counterparts d n x n aT x n 1 x n d n aT x n 1 where sample vector x n 1 x n 1 x n 2 x n p T contains p past samples and a a1a2 . ap T is a vector containing appropriate weights known also as prediction .

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