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Copy pathtables.py
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98 lines (92 loc) · 4.01 KB
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import numpy as np
zigzag_pattern = [
(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, 3), (5, 2), (6, 1), (7, 0), (7, 1), (6, 2), (5, 3), (4, 4),
(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)
]
standard_luminance = 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
], dtype=np.float32).reshape(8, 8)
standard_chrominance = 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
], dtype=np.float32).reshape(8, 8)
#Standard tables for huffman encoding defined by ITU
standard_huffman_dc0 = np.array(
[0, 1, 5, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
np.uint8
)
standard_huffman_dc1 = np.array(
[0, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
np.uint8
)
standard_huffman_ac0 = np.array(
[0, 2, 1, 3, 3, 2, 4, 3, 5, 5, 4, 4, 0, 0, 1, 125,
0x01, 0x02, 0x03, 0x00, 0x04, 0x11, 0x05, 0x12,
0x21, 0x31, 0x41, 0x06, 0x13, 0x51, 0x61, 0x07,
0x22, 0x71, 0x14, 0x32, 0x81, 0x91, 0xa1, 0x08,
0x23, 0x42, 0xb1, 0xc1, 0x15, 0x52, 0xd1, 0xf0,
0x24, 0x33, 0x62, 0x72, 0x82, 0x09, 0x0a, 0x16,
0x17, 0x18, 0x19, 0x1a, 0x25, 0x26, 0x27, 0x28,
0x29, 0x2a, 0x34, 0x35, 0x36, 0x37, 0x38, 0x39,
0x3a, 0x43, 0x44, 0x45, 0x46, 0x47, 0x48, 0x49,
0x4a, 0x53, 0x54, 0x55, 0x56, 0x57, 0x58, 0x59,
0x5a, 0x63, 0x64, 0x65, 0x66, 0x67, 0x68, 0x69,
0x6a, 0x73, 0x74, 0x75, 0x76, 0x77, 0x78, 0x79,
0x7a, 0x83, 0x84, 0x85, 0x86, 0x87, 0x88, 0x89,
0x8a, 0x92, 0x93, 0x94, 0x95, 0x96, 0x97, 0x98,
0x99, 0x9a, 0xa2, 0xa3, 0xa4, 0xa5, 0xa6, 0xa7,
0xa8, 0xa9, 0xaa, 0xb2, 0xb3, 0xb4, 0xb5, 0xb6,
0xb7, 0xb8, 0xb9, 0xba, 0xc2, 0xc3, 0xc4, 0xc5,
0xc6, 0xc7, 0xc8, 0xc9, 0xca, 0xd2, 0xd3, 0xd4,
0xd5, 0xd6, 0xd7, 0xd8, 0xd9, 0xda, 0xe1, 0xe2,
0xe3, 0xe4, 0xe5, 0xe6, 0xe7, 0xe8, 0xe9, 0xea,
0xf1, 0xf2, 0xf3, 0xf4, 0xf5, 0xf6, 0xf7, 0xf8,
0xf9, 0xfa],
np.uint8
)
standard_huffman_ac1 = np.array(
[0, 2, 1, 2, 4, 4, 3, 4, 7, 5, 4, 4, 0, 1, 2, 119,
0x00, 0x01, 0x02, 0x03, 0x11, 0x04, 0x05, 0x21,
0x31, 0x06, 0x12, 0x41, 0x51, 0x07, 0x61, 0x71,
0x13, 0x22, 0x32, 0x81, 0x08, 0x14, 0x42, 0x91,
0xa1, 0xb1, 0xc1, 0x09, 0x23, 0x33, 0x52, 0xf0,
0x15, 0x62, 0x72, 0xd1, 0x0a, 0x16, 0x24, 0x34,
0xe1, 0x25, 0xf1, 0x17, 0x18, 0x19, 0x1a, 0x26,
0x27, 0x28, 0x29, 0x2a, 0x35, 0x36, 0x37, 0x38,
0x39, 0x3a, 0x43, 0x44, 0x45, 0x46, 0x47, 0x48,
0x49, 0x4a, 0x53, 0x54, 0x55, 0x56, 0x57, 0x58,
0x59, 0x5a, 0x63, 0x64, 0x65, 0x66, 0x67, 0x68,
0x69, 0x6a, 0x73, 0x74, 0x75, 0x76, 0x77, 0x78,
0x79, 0x7a, 0x82, 0x83, 0x84, 0x85, 0x86, 0x87,
0x88, 0x89, 0x8a, 0x92, 0x93, 0x94, 0x95, 0x96,
0x97, 0x98, 0x99, 0x9a, 0xa2, 0xa3, 0xa4, 0xa5,
0xa6, 0xa7, 0xa8, 0xa9, 0xaa, 0xb2, 0xb3, 0xb4,
0xb5, 0xb6, 0xb7, 0xb8, 0xb9, 0xba, 0xc2, 0xc3,
0xc4, 0xc5, 0xc6, 0xc7, 0xc8, 0xc9, 0xca, 0xd2,
0xd3, 0xd4, 0xd5, 0xd6, 0xd7, 0xd8, 0xd9, 0xda,
0xe2, 0xe3, 0xe4, 0xe5, 0xe6, 0xe7, 0xe8, 0xe9,
0xea, 0xf2, 0xf3, 0xf4, 0xf5, 0xf6, 0xf7, 0xf8,
0xf9, 0xfa],
np.uint8
)