EZW_Matlab_Demo.rar_DEMO_EZW_EZW图像压缩_ezw compression _matlab EZ

function [signif_map, subordinate_list, data] = func_dominant_pass(img_wavedata, threshold, scan); % % img_wavedata: wavelet coefficients, if this step is not using the initial theshold % the returned 'data' from the previous step should be used (to % correctly process already processed coefficients) % threshold: threshold to use for this step, initial threshold should be % pow2(floor(log2(max(max(abs(img_wavedata)))))) % scan: scan order to use when processing img_wavedata matrix % (currently only Morton scan order supported) % % signif_map: returned string containing the significance info for the data in % img_wavedata using 'threshold' as threshold % 'p' significant positive % 'n' significant negative % 'z' isolated zero % 't' zerotree root % subordinate_list: list containing the coefficients that are detected significant % in _this_ step % first row is the original coefficient % second row is first reconstruction value of this coefficient % data: new wavelet coefficients to use in the next step % % Copyright 2002 Paschalis Tsiaflakis, Jan Vangorp % wavelet coefficients are saved to undo bookkeeping actions data = img_wavedata; dim = size(img_wavedata,1); % significance map signif_map = []; signif_index = 1; % subordinate list subordinate_list = []; subordinate_index = 1; for element = 1:dim*dim; % get matrix index for element row = scan(element,1)+1; column = scan(element,2)+1; % check whether element should be processed if(~isnan(data(row, column)) & data(row, column) < realmax), % determine type of element if(data(row,column) >= threshold), % element is significant positive signif_map(signif_index) = 'p'; signif_index = signif_index + 1; subordinate_list(1, subordinate_index) = data(row, column); % first reconstructed value subordinate_list(2, subordinate_index) = threshold + threshold/2; subordinate_index= subordinate_index + 1; % mark element as processed data(row, column) = 0; elseif(data(row,column) <= -threshold), % element is significant negative signif_map(signif_index) = 'n'; signif_index = signif_index+ 1; subordinate_list(1, subordinate_index) = data(row, column); % first reconstructed value subordinate_list(2, subordinate_index) = -threshold - threshold/2; subordinate_index= subordinate_index + 1; % mark element as processed data(row, column) = 0; else % determine wether element is zerotree root % select EZW tree for element if(row<dim/2 | column<dim/2), mask = func_treemask(row,column,dim); else % shortcut treemask processing (element has no tree under it) if(abs(data(row, column)) < threshold), % element is zerotree root signif_map(signif_index) = 't'; signif_index = signif_index + 1; % mark elements as processed (only for this pass!) data(row, column) = realmax; else % element is isolated zero signif_map(signif_index) = 'z'; signif_index = signif_index + 1; end end masked = data .* mask; % compare data to threshold if(isempty(find(abs(masked) >= threshold))), % element is zerotree root signif_map(signif_index) = 't'; signif_index = signif_index + 1; % mark elements as processed (only for this pass!) data = data + (mask*realmax); else % element is isolated zero signif_map(signif_index) = 'z'; signif_index = signif_index + 1; end end end end index = find(data == realmax); data(index) = img_wavedata(index);