dct.py

import cv2
import numpy as np
import matplotlib.pyplot as plt

# Read image
img = cv2.imread("image.jpg", 0)
img = np.float32(img)

h, w = img.shape

# Function for block-wise DCT compression
def dct_compress(img, thresh=20):
    img_dct = np.zeros((h, w), np.float32)

    for i in range(0, h, 8):
        for j in range(0, w, 8):
            block = img[i:i+8, j:j+8]

            # Apply DCT
            dct_block = cv2.dct(block)

            # Thresholding small values (compression)
            dct_block[np.abs(dct_block) < thresh] = 0

            # Apply inverse DCT
            idct_block = cv2.idct(dct_block)

            img_dct[i:i+8, j:j+8] = idct_block

    return img_dct

compressed_img = dct_compress(img, thresh=40)
compressed_img = np.uint8(compressed_img)

# Display
plt.figure(figsize=(10,5))
plt.subplot(1,2,1); plt.title("Original"); plt.imshow(img, cmap="gray"); plt.axis("off")
plt.subplot(1,2,2); plt.title("DCT Compressed"); plt.imshow(compressed_img, cmap="gray"); plt.axis("off")
plt.show()