File size: 3,663 Bytes
4380e0f |
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 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
import cv2 as cv
import numpy as np
from PIL import Image
DCT_SIZE = 8
TABLE_SIZE = DCT_SIZE ** 2
ZIG_ZAG = [
[0, 0],
[0, 1],
[1, 0],
[2, 0],
[1, 1],
[0, 2],
[0, 3],
[1, 2],
[2, 1],
[3, 0],
[4, 0],
[3, 1],
[2, 2],
[1, 3],
[0, 4],
[0, 5],
[1, 4],
[2, 3],
[3, 2],
[4, 1],
[5, 0],
[6, 0],
[5, 1],
[4, 2],
[3, 3],
[2, 4],
[1, 5],
[0, 6],
[0, 7],
[1, 6],
[2, 5],
[3, 4],
[4, 4],
[5, 3],
[6, 2],
[7, 1],
[7, 2],
[6, 3],
[5, 4],
[4, 5],
[3, 5],
[2, 6],
[1, 7],
[2, 7],
[3, 6],
[4, 5],
[5, 4],
[6, 3],
[7, 2],
[7, 3],
[6, 4],
[5, 5],
[4, 6],
[3, 7],
[4, 7],
[5, 6],
[6, 5],
[7, 4],
[7, 5],
[6, 6],
[5, 7],
[6, 7],
[7, 6],
[7, 7],
]
def compress_jpg(image: Image.Image, quality, color=True):
"""Compress a PIL image to JPEG format with specified quality.
Args:
image: Input PIL image (RGB format)
quality: JPEG compression quality (1-100)
color: Whether to preserve color (BGR format)
Returns:
np.ndarray: Decompressed image in BGR or grayscale format
"""
# Convert PIL image to OpenCV BGR format
img_np = np.array(image)
if color:
img_np = cv.cvtColor(img_np, cv.COLOR_RGB2BGR)
_, buffer = cv.imencode(".jpg", img_np, [cv.IMWRITE_JPEG_QUALITY, quality])
return cv.imdecode(buffer, cv.IMREAD_COLOR if color else cv.IMREAD_GRAYSCALE)
def loss_curve(image: Image.Image, qualities=tuple(range(1, 101)), normalize=True):
"""Calculate JPEG compression loss curve for quality estimation.
Args:
image: Input PIL image (RGB format)
qualities: Quality values to test (1-100)
normalize: Whether to normalize the output curve
Returns:
np.ndarray: Mean absolute difference values across quality levels
"""
# Convert input image to grayscale BGR for compression testing
img_np = np.array(image)
if len(img_np.shape) == 3:
x = cv.cvtColor(img_np, cv.COLOR_RGB2GRAY)
else:
x = img_np
c = np.array(
[cv.mean(cv.absdiff(compress_jpg(x, q, False), x))[0] for q in qualities]
)
if normalize:
c = cv.normalize(c, None, 0, 1, cv.NORM_MINMAX).flatten()
return c
def estimate_qf(image):
return np.argmin(loss_curve(image))
def get_tables(quality):
luma = 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],
]
)
chroma = np.array(
[
[17, 18, 24, 47, 99, 99, 99, 99],
[18, 21, 26, 66, 99, 99, 99, 99],
[24, 26, 56, 99, 99, 99, 99, 99],
[47, 66, 99, 99, 99, 99, 99, 99],
[99, 99, 99, 99, 99, 99, 99, 99],
[99, 99, 99, 99, 99, 99, 99, 99],
[99, 99, 99, 99, 99, 99, 99, 99],
[99, 99, 99, 99, 99, 99, 99, 99],
]
)
quality = np.clip(quality, 1, 100)
if quality < 50:
quality = 5000 / quality
else:
quality = 200 - quality * 2
tables = np.concatenate((luma[:, :, np.newaxis], chroma[:, :, np.newaxis]), axis=2)
tables = (tables * quality + 50) / 100
return np.clip(tables, 1, 255).astype(int)
|