By B.G. Mertzios, P. Liatsis
The papers during this quantity specialise in the main smooth and demanding features of photo and sign Processing and similar components that experience an important effect in our society. The papers could be labeled within the following 4 significant elements. Coding and Compression (image coding, photograph subband, wavelet coding and illustration, video coding, movement estimation and multimedia); snapshot Processing and development attractiveness (image research, aspect detection, segmentation, photograph enhancement and recovery, adaptive structures, color processing, development and item acceptance and classification); quick Processing strategies (computational tools, VLSI DSP architectures); idea and purposes (identificiation and modelling, multirate filter out banks, wavelets in photograph and sign processing, biomedical and business applications). The authors of those enormously high quality papers shape an enticing staff, originating from the 5 continents, representing 33 international locations.
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M. 'An improvement of the minimum distortion encoding algorithm for vector quantization', IEEE Trans. , Vol Com-33, October 1985. K. G. Mertzios and P. V. All rights reserved. 19 Architecture Design for Polynomial Approximation Coding of Image Compression Chung-Yen Lu and Kuei-Ann Wen Abstract Polynomial approximation coding (PAC) is well-known for image compression. 2-D natural polynomial is chosen to approximate the shape of block images, and the polynomial coefficients are completed by regressive method.
Consecutive frames of video sequences usually show similar chrominance histograms and therefore produce codebooks with similar tree structure. This property makes the frame-to-frame comparison of the codebooks easy. This comparison is necessary for the decision whether the codebook (or optionally a part of it) must be retransmitted. This algorithm results in very good codebooks in the sense of total square error (cf. fig. 3). i 9 , 9 9 ~ @ fx~--~"zCk~')l~ O ~ 9 " division line , "for step No. x.
J, relates to both input vector and the neuron weights. It plays an important role in selecting the winner at each iteration. j, are positive, we have: N N (4) ZXniWnj~XmaxZWnj n=l n=l where X,,ax corresponds to the maximum element in the input vector Xi. This can also be pre-selected before the training starts. Therefore, if we define: N w )-IIx, = o(x,. , n=l (5) 17 as the modified distance between Xi and Wj, the equation (4) can be used to define an approximate distance D(Xi, Wj ) as follows: N -D(Xi, % ) = IIw~II= - 2Xmax Z Wnj <- O(Xi, Wj ) (6) n=l II II and Zn=lWnj, can be pre-calculated and stored in the network, equation (6) can be further Since both terms, simplified as: m (7) O( Xi, % ) = Aj - Xmax nj where Aj = and Bj = 2 n=l w,~j.