By Wu F.
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Additional resources for Advances in visual data compression and communication
Let Ts1 s2 si 1 0 be a subtree starting with s1 s2 si 1 0. The probability of this subtree is p(Ts1 s2 si 1 0 ) = ∑ p(s1 s2 si 1 0ui+1 un ) = p(s1 s2 si 1 0). 22) ui+1 un It can be calculated easily. Therefore, we can write F(sn ) as F(sn ) = ∑ un sn p(un ) = p(T ) = ∑ T :T is to the le f t o f S ∑ p(s1 s2 si 1 0). 23) i:si =1 Thus, F(sn ) can be calculated quickly from p(sn ). To encode the next bit of the source sequence, we need only calculate p(sn sn+1 ) and update F(sn sn+1 ) using the above method.
A finished tree has up to q leaf nodes and q − 1 internal nodes. This Huffman tree produces the optimal code length. 2a. 2b. Therefore, the codewords for the symbols a0 , a1 , a2 , and a3 are “1,” “01,” “001,” and “000,” respectively. The shortest codeword “1” is assigned to the symbol a0 . Using Eq. 75 bits, which is exactly equal to the entropy. 2 Huffman coding. (a) The process of building a binary tree; (b) the designed Huffman codes. 2 Source Coding 9 they cannot adjust codeword lengths at fractional bit precision.
The quantization objective is to map DCT coefficients from a large alphabet to a small alphabet. 4. 4 A scalar quantization. 5 The ZigZag scan. the minimum unit is quantized to ∆ . The parameter ∆ is selected according to the compression rate. If the compression rate is low, the parameter ∆ is large and the reconstructed pictures have large distortion. If the compression rate is high, the parameter ∆ is small and we cannot observe the compression distortion. 4 assumes that the coefficients have uniform distribution.
Advances in visual data compression and communication by Wu F.