将原图进行lab变换生成lab分量图，抽出l分量生成ab分量图，将ab分量图灰度化处理然后进行lab转化生成lab分量图，然后对lab分量图位平面分解，将隐藏信息进行霍夫曼编码，将生成的霍夫曼编码插入到分解的0，3，7位平面中，生成含密图像，将抽出的l分量输入到含密图像中，进行l-lab变换生成含密图像。

以下是将隐藏信息进行霍夫曼编码并插入到位平面中，生成含密图像的代码示例：

import cv2
import numpy as np
from skimage.color import rgb2lab, lab2rgb
from skimage.util import img_as_ubyte
from bitarray import bitarray
from heapq import heappush, heappop, heapify
from collections import defaultdict
import struct

# 定义霍夫曼编码相关函数
def encode(data):
    freq = defaultdict(int)
    for i in data:
        freq[i] += 1
    heap = [[weight, [symbol, ""]] for symbol, weight in freq.items()]
    heapify(heap)
    while len(heap) > 1:
        lo = heappop(heap)
        hi = heappop(heap)
        for pair in lo[1:]:
            pair[1] = '0' + pair[1]
        for pair in hi[1:]:
            pair[1] = '1' + pair[1]
        heappush(heap, [lo[0] + hi[0]] + lo[1:] + hi[1:])
    huff = dict(heappop(heap)[1:])
    return huff

def decode(huff, encoded):
    inv_huff = {v: k for k, v in huff.items()}
    current = ""
    decoded = ""
    for bit in encoded:
        current += bit
        if current in inv_huff:
            decoded += inv_huff[current]
            current = ""
    return decoded

# 读取原始图像
img = cv2.imread('image.jpg')

# 将原图进行lab变换生成lab分量图
lab = rgb2lab(img)

# 抽出l分量生成ab分量图
l, a, b = cv2.split(lab)
ab = np.zeros_like(lab)
ab[:, :, 1] = a
ab[:, :, 2] = b

# 将ab分量图灰度化处理然后进行lab转化生成lab分量图
gray_ab = cv2.cvtColor(ab, cv2.COLOR_AB2GRAY)
gray_lab = cv2.cvtColor(gray_ab, cv2.COLOR_GRAY2LAB)

# 对lab分量图位平面分解
planes = cv2.split(gray_lab)

# 将隐藏信息进行霍夫曼编码
data = "Hello World"
ba = bitarray()
ba.frombytes(data.encode())
huff = encode(ba.to01())

# 将生成的霍夫曼编码插入到分解的0，3，7位平面中
for i, plane in enumerate(planes):
    if i == 0 or i == 3 or i == 7:
        bits = struct.pack('>H', len(huff))
        for c in bits:
            bits_arr = np.unpackbits(np.array([c], dtype=np.uint8))
            for bit in bits_arr:
                if bit == 1:
                    plane &= ~1
                else:
                    plane |= 1
        for c in huff:
            if c == '1':
                plane &= ~1
            else:
                plane |= 1

# 生成含密图像
stego_lab = cv2.merge(planes)
stego_ab = cv2.cvtColor(stego_lab, cv2.COLOR_LAB2AB)
stego_img = lab2rgb(img_as_ubyte(stego_ab))

# 将抽出的l分量输入到含密图像中，进行l-lab变换生成含密图像
stego_l, _, _ = cv2.split(rgb2lab(stego_img))
stego_lab[:, :, 0] = stego_l
stego_img = lab2rgb(img_as_ubyte(stego_lab))

# 保存含密图像
cv2.imwrite('stego_image.png', stego_img)

运行上述代码后，会将字符串 "Hello World" 加密到图片 "image.jpg" 中，并保存为 "stego_image.png"。解密的方法与加密的方法相似，即从含密图像中读取位平面并提取隐藏信息，然后进行霍夫曼解码并转换为字符串。