编写程序(C或Matlab),给定一幅CIF格式(352×288)灰度图像(无色度信号),记为试验帧(Test),对应文件为Test.dat。要求: 对试验帧中每一个8×8的块进行DCT,至少设计两个量化表分别进行量化,对量化后的结果进行DCT逆变换,结果取整为Result图像,并加以显示,体会不同的量化步长的选择对图像质量的影响。
时间: 2023-12-23 12:04:28 浏览: 25
以下是Matlab的代码实现:
```matlab
% 读取测试帧数据
fid = fopen('Test.dat', 'r');
test = fread(fid, [352, 288], 'uint8')';
fclose(fid);
% 定义DCT变换矩阵
dct_matrix = dctmtx(8);
% 定义量化表,量化步长为10和20
quant_table1 = [80, 60, 50, 80, 120, 200, 255, 255;
55, 60, 70, 95, 130, 255, 255, 255;
70, 65, 80, 120, 200, 255, 255, 255;
70, 85, 110, 145, 255, 255, 255, 255;
90, 110, 185, 255, 255, 255, 255, 255;
120, 175, 255, 255, 255, 255, 255, 255;
245, 255, 255, 255, 255, 255, 255, 255;
255, 255, 255, 255, 255, 255, 255, 255];
quant_table2 = [16, 11, 10, 16, 24, 40, 51, 61;
12, 12, 14, 19, 26, 58, 60, 55;
14, 13, 16, 24, 40, 57, 69, 56;
14, 17, 22, 29, 51, 87, 80, 62;
18, 22, 37, 56, 68, 109, 103, 77;
24, 35, 55, 64, 81, 104, 113, 92;
49, 64, 78, 87, 103, 121, 120, 101;
72, 92, 95, 98, 112, 100, 103, 99];
% 对每个8×8块进行DCT和量化
quantized1 = zeros(288, 352);
quantized2 = zeros(288, 352);
for i = 1:8:281
for j = 1:8:345
block = double(test(i:i+7, j:j+7));
dct_result = dct_matrix * block * dct_matrix';
quantized1(i:i+7, j:j+7) = round(dct_result ./ quant_table1);
quantized2(i:i+7, j:j+7) = round(dct_result ./ quant_table2);
end
end
% 对量化结果进行IDCT反变换
result1 = zeros(288, 352);
result2 = zeros(288, 352);
for i = 1:8:281
for j = 1:8:345
quantized_block1 = quantized1(i:i+7, j:j+7) .* quant_table1;
quantized_block2 = quantized2(i:i+7, j:j+7) .* quant_table2;
idct_result1 = dct_matrix' * quantized_block1 * dct_matrix;
idct_result2 = dct_matrix' * quantized_block2 * dct_matrix;
result1(i:i+7, j:j+7) = round(idct_result1);
result2(i:i+7, j:j+7) = round(idct_result2);
end
end
% 显示结果图像
figure;
subplot(131);
imshow(test);
title('原图');
subplot(132);
imshow(result1);
title('量化步长为10');
subplot(133);
imshow(result2);
title('量化步长为20');
```
这段代码首先读取了CIF格式的灰度图像数据,然后定义了DCT变换矩阵和两个量化表。接着,对每个8×8块进行DCT和量化,并对量化结果进行IDCT反变换得到结果图像。最后,使用Matlab的`imshow`函数显示原图和两个量化步长的结果图像。