数字图像处理实验6 冈萨雷斯

实验六图像压缩（同预习）

一、实验目的

1、理解有损压缩和无损压缩的概念；

2、理解图像压缩的主要原则和目的；

3、利用MATLAB 程序进行图像压缩。

二、实验内容

1、Huffman 编码

使用mat2huff，huff2mat 实现Huffman 编解码，并应用imratio 计算编码后信号的压缩率。

2、编写无损预测编解码（lossless predictive coding）程序，使p313 Figure 8.7 (c)

图具有更低的熵（5.4436）。显示预测误差(prediction error)图。用解码程序解码，并验证解码是否正确（compare）。

3、参考p319-323，实现16×16分块离散余弦(DCT)编码，用zigzag方式保留每一

块的前10个和前21个系数，再进行进行DCT解码。编写程序并分别显示解码后的两个图。

实验程序：

实验1：

i=imread('Fig0804(a)(Tracy).tif'); i1=mat2huff(i);

cr1=imratio(i,i1);%cr1,压缩率

i2=huff2mat(i1);

rmse1=compare(i,i2);

figure,imshow(i);

figure,imshow(i2);

实验2：

子程序1：

function y = mat2lpc2(x, f)

%本程序改自课本上那个程序，可实现更高压缩率！

error(nargchk(1, 2, nargin)); % Check input arguments

if nargin < 2 % Set default filter if omitted

f = 1;

end

x = double(x); % Ensure double for computations [m, n] = size(x); % Get dimensions of input matrix

p = zeros(m, n); % Init linear prediction to 0

xs = x; zc = zeros(1,n); % Prepare for input shift and pad for j = 1:length(f) % For each filter coefficient ...

xs = [zc; xs(1:end - 1,:)]; % Shift and zero pad xga改加一行 p = p + f(j) * xs; % Form partial prediction sums

end

y = x - round(p); % Compute the prediction error

子程序2:

function x = lpc2mat2(y, f)

% 本程序改自课本上那个程序，可以解压

error(nargchk(1, 2, nargin)); % Check input arguments

if nargin < 2 % Set default filter if omitted

f = 1;

end

y=y';

f = f(end:-1:1); % Reverse the filter coefficients

[m, n] = size(y); % Get dimensions of output matrix order = length(f); % Get order of linear predictor

f = repmat(f, m, 1); % Duplicate filter for vectorizing

x = zeros(m, n + order); % Pad for 1st 'order' column decodes

% Decode the output one column at a time. Compute a prediction based % on the 'order' previous elements and add it to the prediction

% error. The result is appended to the output matrix being built.

for j = 1:n

jj = j + order;

x(:, jj) = y(:, j) + round(sum(f(:, order:-1:1) .* ...

x(:, (jj - 1):-1:(jj - order)), 2));

end

x = x(:, order + 1:end); % Remove left padding

x=x';

总程序：

f=imread('Fig0807(c)(Aligned).tif');

e=mat2lpc2(f);

figure,imshow(mat2gray(e));

h=entropy(e);

c=lpc2mat2(e);

figure,imshow(c,[]);

rmse=compare(f,c);

实验三：

i=imread('Fig0804(a)(Tracy).tif');

i=im2double(i);

mask1=[1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ];

mask2=[1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0