[Chapter 13 image compression and reconstruction based on Hoffman -- image processing application of MATLAB deep learning practice]

Image compression and reconstruction based on Huffman
Some reference sources ：
https://blog.csdn.net/qq_59747472/article/details/121890265

To save space , When encoding data , You can specify fewer digits for data that often occurs , Those data that do not often appear specify more digits to represent , This reduces redundancy , In this way, the overall effect is to save storage space . The basic principle of image compression based on Huffman coding is to replace frequently used data with shorter code , Less used data is replaced by longer code , The code of each data is different , This is a typical lossless coding method .

Partial function

Mat2Huff.m

function [zvec, zi] = Mat2Huff(vec)

if ~isa(vec,'uint8')
    fprintf('\n Please confirm the input uint8 Type data vector ！\n');
    return;
end

vec = vec(:)';
f = Frequency(vec);
syminfos = find(f~=0); 
f = f(syminfos);
[f, sind] = sort(f);
syminfos = syminfos(sind);
len = length(syminfos);
syminfos_ind = num2cell(1:len);
cw_temp = cell(len,1);
while length(f)>1
    ind1 = syminfos_ind{
    1};
    ind2 = syminfos_ind{
    2};
    cw_temp(ind1) = AddNode(cw_temp(ind1),uint8(0));
    cw_temp(ind2) = AddNode(cw_temp(ind2),uint8(1));
    f = [sum(f(1:2)) f(3:end)];
    syminfos_ind = [{
    [ind1 ind2]} syminfos_ind(3:end)];
    [f,sind] = sort(f);
    syminfos_ind = syminfos_ind(sind);
end
cw = cell(256,1);
cw(syminfos) = cw_temp;
len = 0;
for i = 1 : length(vec),
    len = len+length(cw{
    double(vec(i))+1});
end
str_temp = repmat(uint8(0),1,len);
pt = 1;
for index=1:length(vec)
    cd = cw{
    double(vec(index))+1};
    len = length(cd);
    str_temp(pt+(0:len-1)) = cd;
    pt = pt+len;
end
len = length(str_temp);
pad = 8-mod(len,8);
if pad > 0
    str_temp = [str_temp uint8(zeros(1,pad))];
end
cw = cw(syminfos);
cl = zeros(size(cw));
ws = 2.^(0:51);
mcl = 0;
for index = 1:length(cw)
    len = length(cw{
    index});
    if len>mcl
        mcl = len;
    end
    if len>0
        cd = sum(ws(cw{
    index}==1));
        cd = bitset(cd,len+1);
        cw{
    index} = cd;
        cl(index) = len;
    end
end
cw = [cw{
    :}];
cols = length(str_temp)/8;
str_temp = reshape(str_temp,8,cols);
ws = 2.^(0:7);
zvec = uint8(ws*double(str_temp));
huffcodes = sparse(1,1);
for index = 1:numel(cw)
    huffcodes(cw(index),1) = syminfos(index);
end
zi.pad = pad;
zi.huffcodes = huffcodes;
zi.ratio = cols./length(vec);
zi.length = length(vec);
zi.maxcodelen = mcl;

Histogram comparison function code
HisteqContrast.m

function HisteqContrast(Img1, Img2)
figure('Name', ' Histogram comparison ', 'NumberTitle', 'Off', ...
    'Units', 'Normalized', 'Position', [0.1 0.1 0.5 0.5]);
subplot(2, 2, 1); imshow(mat2gray(Img1)); title(' Original image ', 'FontWeight', 'Bold');
subplot(2, 2, 2); imshow(mat2gray(Img2)); title(' Processed image ', 'FontWeight', 'Bold');
if ndims(Img1) == 3
    Q = rgb2gray(Img1);
else
    Q = mat2gray(Img1);
end

if ndims(Img2) == 3
    W = rgb2gray(Img2);
else
    W = mat2gray(Img2);
end

subplot(2, 2, 3); imhist(Q, 64); title(' Original gray histogram ', 'FontWeight', 'Bold');
subplot(2, 2, 4); imhist(W, 64); title(' Processed gray histogram ', 'FontWeight', 'Bold');

These codes are binary codes , And the codeword length is uneven 、 An encoding in which the average bit rate is close to the entropy of the information source . The encoding process is to scan the image data first , Calculate the probability of various pixels , The optimal binary tree is established according to the size of probability ( The leaf node of the binary tree just represents a certain pixel in the image ) And assign specific weights to each branch of the binary tree (0 or 1), Then read the path weight string from the root node to the leaf node by traversing the binary tree , That is, each pixel is assigned a unique encoding of different lengths , Thus, a Huffman coding table of all pixels of the image is obtained . The encoded image data records the codeword of each pixel , The corresponding relationship between the codeword and the actual pixel value is recorded in the code table , The code table is attached to the image file .



Get the subsequent histogram comparison , The effect is as shown in the picture

Algorithm flow

System GUI Initial interface

Save screenshot function ：

function SnapImage()

imagesPath = '.\\snap_images';
if ~exist(imagesPath, 'dir')
    mkdir(imagesPath);
end

[FileName,PathName,FilterIndex] = uiputfile({
    '*.jpg;*.tif;*.png;*.gif','All Image Files';...
          '*.*','All Files' },' Save the screenshot ',...
          '.\\snap_images\\temp.jpg');
if isequal(FileName, 0) || isequal(PathName, 0)
    return;
end
fileStr = fullfile(PathName, FileName);
f = getframe(gcf);
f = frame2im(f);
imwrite(f, fileStr);
msgbox(' The snapshot file was saved successfully ！', ' Prompt information ');

Save image function ：

function SaveImage(Img)

imagesPath = '.\\results';
if ~exist(imagesPath, 'dir')
    mkdir(imagesPath);
end

[FileName,PathName,FilterIndex] = uiputfile({
    '*.jpg;*.tif;*.png;*.gif','All Image Files';...
          '*.*','All Files' },' Save the screenshot ',...
          '.\\results\\result.jpg');
if isequal(FileName, 0) || isequal(PathName, 0)
    return;
end
fileStr = fullfile(PathName, FileName);
imwrite(mat2gray(Img), fileStr);

Result display ：


Source code of this article Download address –> Portal