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iuyaiuya
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Codec finished (TD3)
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‎Codec/codec.m

Lines changed: 82 additions & 41 deletions
Original file line numberDiff line numberDiff line change
@@ -14,20 +14,20 @@
1414
sub23 = lena_haar(257:512,257:512);
1515
figure
1616
subplot(4,4,1)
17-
imshow(sub11,[0,255])
17+
imshow(sub11)
1818
subplot(4,4,2)
19-
imshow(sub12,[0,255])
19+
imshow(sub12)
2020
subplot(4,4,5)
21-
imshow(sub13,[0,255])
21+
imshow(sub13)
2222
subplot(4,4,6)
23-
imshow(sub14,[0,255])
23+
imshow(sub14)
2424
% Now for the bigger subbands we are going to use bigger subplots
2525
subplot(4,4,[3:4 7:8])
26-
imshow(sub21,[0,255])
26+
imshow(sub21)
2727
subplot(4,4,[9:10 13:14])
28-
imshow(sub22,[0,255])
28+
imshow(sub22)
2929
subplot(4,4,[11:12 15:16])
30-
imshow(sub23,[0,255])
30+
imshow(sub23)
3131

3232
% We are going to use the same subplot distribution to show the histogram
3333
% of each sub image
@@ -57,42 +57,83 @@
5757
% we only have a uniform scalar quantizer, we will use that.
5858
% No Quantization for the ll part of the image (sub 11)
5959

60-
61-
6260
% Need to count all the appearances of each number
6361
entropies = zeros(7,8);
64-
for i= 7:-1:1
65-
q_sub11 = quantize_matrix(sub11,8);
66-
q_sub12 = quantize_matrix(sub12,i);
67-
q_sub13 = quantize_matrix(sub13,i);
68-
q_sub14 = quantize_matrix(sub14,i);
69-
q_sub21 = quantize_matrix(sub21,i);
70-
q_sub22 = quantize_matrix(sub22,i);
71-
q_sub23 = quantize_matrix(sub23,i);
72-
e_sub11 = shannonEntropy(q_sub11);
73-
e_sub12 = shannonEntropy(q_sub12);
74-
e_sub13 = shannonEntropy(q_sub13);
75-
e_sub14 = shannonEntropy(q_sub14);
76-
e_sub21 = shannonEntropy(q_sub21);
77-
e_sub22 = shannonEntropy(q_sub22);
78-
e_sub23 = shannonEntropy(q_sub23);
79-
q_lena = quantize_matrix(lena_haar,i);
80-
q_lena(1:128,1:128) = q_sub11;
81-
e_lena = shannonEntropy(q_lena);
82-
dict = huffmanDict(q_lena);
83-
sizeMap = containers.Map('KeyType','double', 'ValueType','any');
84-
for row = 1:size(dict,1)
85-
keyCell = dict(row,1);
86-
valueCell = dict(row,2);
87-
code = valueCell{1};
88-
key = keyCell{1};
89-
sizeMap(key) = size(code,2);
90-
end
91-
%SizeMap holds the lengths of all the symbols
92-
% TODO subtitute the symbols by their length and add all the elements
93-
% of the matrix, then divide by the nuelem of the matrix
94-
entropies(i,:) = [e_sub11, e_sub12, e_sub13, e_sub14, e_sub21, e_sub22, e_sub23, e_lena];
95-
end
62+
q_sub11 = sub11;
63+
q_sub12 = quantize_matrix(sub12,7);
64+
q_sub13 = quantize_matrix(sub13,7);
65+
q_sub14 = quantize_matrix(sub14,6);
66+
q_sub21 = quantize_matrix(sub21,5);
67+
q_sub22 = quantize_matrix(sub22,5);
68+
q_sub23 = quantize_matrix(sub23,4);
69+
70+
e_sub11 = shannonEntropy(q_sub11);
71+
h_sub11 = numel(huffmanCode(q_sub11))/numel(q_sub11);
72+
73+
e_sub12 = shannonEntropy(q_sub12);
74+
h_sub12 = numel(huffmanCode(q_sub12))/numel(q_sub12);
75+
76+
e_sub13 = shannonEntropy(q_sub13);
77+
h_sub13 = numel(huffmanCode(q_sub13))/numel(q_sub13);
78+
79+
e_sub14 = shannonEntropy(q_sub14);
80+
h_sub14 = numel(huffmanCode(q_sub14))/numel(q_sub14);
81+
82+
e_sub21 = shannonEntropy(q_sub21);
83+
h_sub21 = numel(huffmanCode(q_sub21))/numel(q_sub21);
84+
85+
e_sub22 = shannonEntropy(q_sub22);
86+
h_sub22 = numel(huffmanCode(q_sub22))/numel(q_sub22);
87+
88+
e_sub23 = shannonEntropy(q_sub23);
89+
h_sub23 = numel(huffmanCode(q_sub23))/numel(q_sub23);
90+
91+
% We need to "stitch" all the pieces of q_lena together
92+
q_lena(1:128,1:128) = q_sub11;
93+
q_lena(1:128,129:256) = q_sub12;
94+
q_lena(129:256,1:128) = q_sub13;
95+
q_lena(129:256,129:256) = q_sub14;
96+
q_lena(1:256,257:512) = q_sub21;
97+
q_lena(257:512,1:256) = q_sub22;
98+
q_lena(257:512,257:512) = q_sub23;
99+
e_lena = shannonEntropy(q_lena);
100+
h_lena = numel(huffmanCode(q_lena))/numel(q_lena); %q_lena in huffman code
101+
102+
%sizeMap = containers.Map('KeyType','double', 'ValueType','any');
103+
%for row = 1:size(dict,1)
104+
% keyCell = dict(row,1);
105+
% valueCell = dict(row,2);
106+
% code = valueCell{1};
107+
% key = keyCell{1};
108+
% sizeMap(key) = size(code,2);
109+
%end
110+
111+
%SizeMap holds the lengths of all the symbols
112+
% TODO subtitute the symbols by their length and add all the elements
113+
% of the matrix, then divide by the nuelem of the matrix
114+
entropies = [e_sub11, e_sub12, e_sub13, e_sub14, e_sub21, e_sub22, e_sub23, e_lena;
115+
h_sub11, h_sub12, h_sub13, h_sub14, h_sub21, h_sub22, h_sub23, h_lena]';
116+
96117
entropies
97118

119+
%%%%%%%% SYNTHESIS %%%%%%%%%%%%%
120+
dq_lena(1:128,1:128) = q_lena(1:128,1:128);
121+
dq_lena(1:128,129:256) = dequantize_matrix(q_lena(1:128,129:256),7,8);
122+
dq_lena(129:256,1:128) = dequantize_matrix(q_lena(129:256,1:128),7,8);
123+
dq_lena(129:256,129:256) = dequantize_matrix(q_lena(129:256,129:256),6,8);
124+
dq_lena(1:256,257:512) = dequantize_matrix(q_lena(1:256,257:512),5,8);
125+
dq_lena(257:512,1:256) = dequantize_matrix(q_lena(257:512,1:256),5,8);
126+
dq_lena(257:512,257:512) = dequantize_matrix(q_lena(257:512,257:512),4,8);
127+
128+
synth_lena = haar_reverse_multilevel(dq_lena,2);
129+
imshow(synth_lena)
130+
131+
%%%%%%%% PSNR calculation %%%%%%%%
132+
[PSNR,MSE,MAXERR,L2RAT] = measerr(lena_gray_512,synth_lena)
133+
% measerr already provides the PSNR but we can calculate it again using the
134+
% formula: 10 * log10((255^2)/MSE)
135+
PSNR_2 = 10 * log10((255^2)/MSE);
136+
% Which gives the exact same result
137+
[PSNR, PSNR_2]
98138

139+
% END OF FILE

‎Codec/huffmanDict.m renamed to ‎Codec/huffmanCode.m

Lines changed: 2 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,7 +1,8 @@
1-
function dict = huffmanDict(matrix)
1+
function code = huffmanCode(matrix)
22
a = unique(matrix);
33
count = numel(matrix);
44
out = [a,histc(matrix(:),a)/count];
55
p = out(:,2);
66
dict = huffmandict(a,p);
7+
code = huffmanenco(matrix(:),dict);
78
end

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