DCT用于图像压缩的代码
时间: 2023-11-21 14:04:10 浏览: 50
这里是一个用Python实现的DCT图像压缩代码:
```python
import numpy as np
import cv2
def block_dct(block):
"""
对8x8块进行DCT变换
"""
return cv2.dct(block.astype(np.float32))
def block_idct(block):
"""
对8x8块进行IDCT变换
"""
return cv2.idct(block.astype(np.float32))
def quantize(block, q_table):
"""
对8x8块进行量化
"""
return np.round(block / q_table).astype(np.int32)
def dequantize(block, q_table):
"""
对8x8块进行反量化
"""
return block * q_table
def compress(img, q_table):
"""
对图像进行压缩
"""
height, width = img.shape
compressed = np.zeros_like(img, dtype=np.int32)
for i in range(0, height, 8):
for j in range(0, width, 8):
block = img[i:i+8, j:j+8]
dct = block_dct(block)
quantized = quantize(dct, q_table)
compressed[i:i+8, j:j+8] = quantized
return compressed
def decompress(compressed, q_table):
"""
对压缩后的数据进行解压缩
"""
height, width = compressed.shape
decompressed = np.zeros_like(compressed, dtype=np.float32)
for i in range(0, height, 8):
for j in range(0, width, 8):
block = compressed[i:i+8, j:j+8]
dequantized = dequantize(block, q_table)
idct = block_idct(dequantized)
decompressed[i:i+8, j:j+8] = idct
return np.clip(decompressed, 0, 255).astype(np.uint8)
# 测试代码
if __name__ == '__main__':
img = cv2.imread('lena.png', cv2.IMREAD_GRAYSCALE)
q_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]])
compressed = compress(img, q_table)
decompressed = decompress(compressed, q_table)
cv2.imshow('original', img)
cv2.imshow('decompressed', decompressed)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
这个代码实现了基于DCT的图像压缩,对于一张256x256的灰度图像,它将被分成32x32个8x8的块,对每个块进行DCT变换,然后进行量化,最后将量化后的数据存储成一个矩阵。解压缩时,将量化后的矩阵反量化,再进行IDCT变换,最后将每个块的结果合并成一张图像。在这个实现中,我们使用了一个标准的量化表,它是JPEG标准中的一个常用量化表。