外文翻譯外文文獻英文翻譯一種基于嵌入式零樹小波算法的魯棒圖像壓縮新方法-文庫吧
2024-10-27 20:01 本頁面
【正文】 he sign and position of all significant coefficients. This symbol can be a + or indicating the sign of the significant coefficient。 a “0” indicating that the coefficient is insignificant。 or a zerotree root (ZTR) indicating that the coefficient is insignificant along with all of the finer resolution coefficients corresponding to the same spatial region. The inclusion of the ZTR symbol greatly increases the coding efficiency because it allows the encoder to exploit interscale correlations that have been observed in most images . After puting the “significance map” symbols for a given bit plane, resolution enhancement bits must be transmitted for all significant 3 coefficients。 in our implementation, we concatenate two of these to form a symbol. Prior to transmission, the significance and resolution enhancement symbols are arithmetically encoded using the simple adaptive model described in with a four symbol alphabet (plus one stop symbol). The threshold T is then divided by two, and the scanning process is repeated until some rate or distortion target is met. At this point, the stop symbol is transmitted. The decoder, on the other hand, simply accepts the bitstream ing from the encoder, arithmetically decodes it, and progressively builds up the significance map and enhancement list in the exact same way as they were created by the encoder. The embedded nature of the bitstream produced by this encoder provides a certain degree of error protection. Specifically, all of the information which arrives before the first bit error occurs can be used to reconstruct the image。 everything that arrives after is lost. This is in direct contrast to many pression algorithms where a single error can irreparably damage the image. Furthermore, we have found that the EZW algorithm can actually detect an error when its arithmetic decoder terminates (by decoding a stop symbol) before reaching its target rate or distortion. It is easy to see why this must happen. Consider that the encoder and decoder use the same backward adaptive model to calculate the probabilities of the five possible symbols (four data symbols plus the stop symbol) and that these probabilities directly define the codewords. Not surprisingly, the length of a symbol’s codeword is 4 inversely proportional to its probability. If a pletely random bit sequence is fed into the arithmetic decoder, then the probability of decoding any symbol is pletely determined by the initial state of the adaptive model—., the probability weighting defined by the model is not, on the average, changed by a random input. In our implementation of the Witten et al. arithmetic coder, we set Max_frequency equal to 500 and maintain the stop symbol probability at 1/cum_freq. Because cum_freq (the sum of the frequency counts of all symbols) is divided by two whenever it exceeds Max_frequency, the probability of decoding a stop symbol stays m
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