请对一个RBG图像手动实现JPEG压缩，复杂的过程可以使用库函数，Python实现，并请详细注释代码意思

首先，我们需要导入必要的库和读入一张RGB图像。这里使用Python的Pillow库来读入图像。

from PIL import Image
import numpy as np

# 读入RGB图像
img = Image.open('test.jpg').convert('RGB')

接下来，我们需要将RGB图像进行分块并对每个分块进行离散余弦变换（Discrete Cosine Transform, DCT）。这里我们使用Scipy库中的DCT函数来实现。具体过程是将RGB图像拆分成8x8的小块，对每个小块进行DCT变换，并将结果存储在8x8的矩阵中。

from scipy.fftpack import dct

# 拆分成8x8小块
blocks = []
for i in range(0, img.height-7, 8):
    for j in range(0, img.width-7, 8):
        blocks.append(np.asarray(img.crop((j, i, j+8, i+8))))

# 对每个小块进行DCT变换
dct_blocks = []
for block in blocks:
    dct_block = np.zeros((8, 8, 3))
    for i in range(3):
        dct_block[:, :, i] = dct(dct(block[:, :, i], axis=0, norm='ortho'), axis=1, norm='ortho')
    dct_blocks.append(dct_block)

接下来，我们需要对每个小块进行量化（Quantization）。这里我们使用一个8x8的量化矩阵，该矩阵中的每个元素代表对应位置的系数在量化后的值。量化的过程就是将DCT变换后得到的矩阵与量化矩阵相乘得到量化后的矩阵。

# 量化矩阵
quantization_matrix = 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]])

# 对每个小块进行量化
quantized_blocks = []
for block in dct_blocks:
    quantized_block = np.zeros((8, 8, 3))
    for i in range(3):
        quantized_block[:, :, i] = np.round(block[:, :, i] / quantization_matrix)
    quantized_blocks.append(quantized_block)

接下来，我们需要对量化后的小块进行编码。这里我们采用霍夫曼编码（Huffman Encoding）的方法。具体过程是将每个小块的系数按照霍夫曼编码表进行编码，并将编码结果存储到二进制文件中。

from collections import Counter

# 构建霍夫曼编码表
def build_huffman_table(freq_dict):
    nodes = []
    for symbol, freq in freq_dict.items():
        nodes.append((freq, symbol, ''))
    while len(nodes) > 1:
        nodes = sorted(nodes, key=lambda x: x[0])
        left = nodes[0]
        right = nodes[1]
        for node in left[1:]:
            node[2] = '0' + node[2]
        for node in right[1:]:
            node[2] = '1' + node[2]
        nodes = [(left[0] + right[0], left, right)] + nodes[2:]
    huffman_table = {}
    for node in nodes[0][1:]:
        huffman_table[node[1]] = node[2]
    return huffman_table

# 编码并保存到二进制文件
encoded_blocks = []
with open('output.bin', 'wb') as f:
    for block in quantized_blocks:
        # 将矩阵展开成一维数组
        flattened = block.flatten().tolist()
        # 统计每个系数出现次数
        freq_dict = Counter(flattened)
        # 构建霍夫曼编码表
        huffman_table = build_huffman_table(freq_dict)
        # 将每个系数按照编码表进行编码
        encoded = ''
        for symbol in flattened:
            encoded += huffman_table[symbol]
        # 将编码结果转换为二进制并写入文件
        while len(encoded) % 8 != 0:
            encoded += '0'
        encoded_bytes = bytes([int(encoded[i:i+8], 2) for i in range(0, len(encoded), 8)])
        f.write(encoded_bytes)

最后，我们需要将编码后的二进制文件进行解码，并还原成RGB图像。解码的过程就是将二进制文件读入并按照霍夫曼编码表进行解码，将解码后的系数乘以量化矩阵得到DCT系数，然后将DCT系数进行逆DCT变换得到原始的RGB图像。

from bitarray import bitarray

# 从二进制文件中读取编码结果
encoded_blocks = []
with open('output.bin', 'rb') as f:
    encoded_bytes = f.read()
    encoded_bits = bitarray()
    encoded_bits.frombytes(encoded_bytes)
    encoded = encoded_bits.to01()

# 解码
decoded_blocks = []
for i in range(len(quantized_blocks)):
    block = np.zeros((8, 8, 3))
    # 将编码结果按照霍夫曼编码表进行解码
    current_code = ''
    for j in range(8*8*3):
        current_code += encoded[j]
        if current_code in huffman_table:
            block.flat[j] = int(huffman_table[current_code])
            current_code = ''
    # 将解码后的系数乘以量化矩阵得到DCT系数
    dct_block = np.zeros((8, 8, 3))
    for k in range(3):
        dct_block[:, :, k] = block[:, :, k] * quantization_matrix
    # 将DCT系数进行逆DCT变换得到原始的RGB图像
    decoded_block = np.zeros((8, 8, 3))
    for l in range(3):
        decoded_block[:, :, l] = dct(dct_block[:, :, l], axis=0, norm='ortho')
        decoded_block[:, :, l] = dct(decoded_block[:, :, l], axis=1, norm='ortho')
    decoded_blocks.append(decoded_block)

# 还原RGB图像
decoded_img = Image.new('RGB', (img.width, img.height))
index = 0
for i in range(0, img.height-7, 8):
    for j in range(0, img.width-7, 8):
        decoded_img.paste(Image.fromarray(np.uint8(decoded_blocks[index])), (j, i))
        index += 1

# 保存还原后的RGB图像
decoded_img.save('decoded.jpg')

完整代码如下：