-
Notifications
You must be signed in to change notification settings - Fork 0
/
LocalDCT.py
82 lines (66 loc) · 3.12 KB
/
LocalDCT.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
from __future__ import division
import numpy as np
from scipy.fftpack import dct
from scipy.fftpack import idct
class LocalDCT:
def __init__(self, image, matrix_qn, value_n):
# Initialize variables
self.matrix_qn = matrix_qn
self.image = image
self.value_n = value_n
def local_dct(self):
# Execute DCT2 in all arrays 8*N
for x in range(0, self.image.shape[0], 8 * self.value_n):
for y in range(0, self.image.shape[1], 8 * self.value_n):
matrix = self.image[x: x + 8 * self.value_n, y: y + 8 * self.value_n]
self.image[x: x + 8 * self.value_n, y: y + 8 * self.value_n] = self.l_dct(matrix)
def local_idct(self):
# Execute I-DCT2 in all arrays 8*N
for x in range(0, self.image.shape[0], 8 * self.value_n):
for y in range(0, self.image.shape[1], 8 * self.value_n):
matrix = self.image[x: x + 8 * self.value_n, y: y + 8 * self.value_n]
self.image[x: x + 8 * self.value_n, y: y + 8 * self.value_n] = self.l_idct(matrix)
# Check values
for i in range(len(self.image)):
for j in range(len(self.image[0])):
if self.image[i][j] < 0:
self.image[i][j] = 0
if self.image[i][j] > 255:
self.image[i][j] = 255
def local_quantization(self):
for x in range(0, self.image.shape[0], 8 * self.value_n):
for y in range(0, self.image.shape[1], 8 * self.value_n):
matrix = self.image[x: x + 8 * self.value_n, y: y + 8 * self.value_n]
self.image[x: x + 8 * self.value_n, y: y + 8 * self.value_n] = self.l_qnt(matrix)
def local_dequantization(self):
for x in range(0, self.image.shape[0], 8 * self.value_n):
for y in range(0, self.image.shape[1], 8 * self.value_n):
matrix = self.image[x: x + 8 * self.value_n, y: y + 8 * self.value_n]
self.image[x: x + 8 * self.value_n, y: y + 8 * self.value_n] = self.l_deqnt(matrix)
# Compute DCT2
@staticmethod
def l_dct(matrix):
return dct(dct(matrix, axis=1, norm='ortho'), axis=0, norm='ortho')
# Compute I-DCT2
@staticmethod
def l_idct(matrix):
return idct(idct(matrix, axis=0, norm='ortho'), axis=1, norm='ortho')
def l_qnt(self, matrix):
for x in range(0, matrix.shape[0]):
for y in range(0, matrix.shape[1]):
if self.matrix_qn[x][y] != 0:
div = matrix[x][y] // self.matrix_qn[x][y]
if np.fabs(matrix[x][y] - self.matrix_qn[x][y] * div) > np.fabs(matrix[x][y] - self.matrix_qn[x][y] * (div + 1)):
matrix[x][y] = div + 1
else:
matrix[x][y] = div
else:
matrix[x][y] = 0
return matrix
def l_deqnt(self, matrix):
for x in range(0, matrix.shape[0]):
for y in range(0, matrix.shape[1]):
matrix[x][y] = matrix[x][y] * self.matrix_qn[x][y]
return matrix
def get_image_compressed(self):
return self.image