用Python写一个灰度图像的编码过程: 1. 构造8x8的子图；2、各像素值减去128；3、DCT正向变换；4、量化；5、“z”字行重排序并将其存入dct.my,使每一步的结果都输出

对于您的问题，我可以回答如下：

1. 构造8x8的子图：

import numpy as np

# 构造8x8的子图
img = np.random.randint(, 256, size=(8, 8))

2. 各像素值减去128：

# 各像素值减去128
img = img - 128

3. DCT正向变换：

# DCT正向变换
from scipy.fftpack import dct

dct_img = dct(dct(img.T, norm='ortho').T, norm='ortho')

4. 量化：

# 量化
quantization_table = np.array([[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]])

quantized_img = np.round(dct_img / quantization_table)

5. “z”字行重排序并将其存入dct.my：

# “z”字行重排序并将其存入dct.my
dct_my = np.zeros((8, 8))
for i in range(8):
    for j in range(8):
        if (i + j) % 2 == :
            dct_my[i, j] = quantized_img[j, i - j + 7]
        else:
            dct_my[i, j] = quantized_img[i - j + 7, j]

print(dct_my)