写一个在jupyter notebooks上运行的对彩色图像进行游程编码的图像压缩算法

好的，下面是一个对彩色图像进行游程编码的图像压缩算法实现。这个算法将彩色图像的 RGB 三个通道分别进行压缩，然后将三个压缩后的游程序列合并。这个算法同样是一种无损压缩算法，可以还原原始图像。

import numpy as np

def run_length_encoding(image):
    # 将图像像素按行扫描
    pixels = image.flatten()
    # 计算游程
    runs = []
    run = [pixels[0], 0]
    for pixel in pixels:
        if pixel == run[0]:
            run[1] += 1
        else:
            runs.append(run)
            run = [pixel, 1]
    runs.append(run)
    # 将游程序列进行压缩
    compressed = []
    for run in runs:
        compressed.append(run[0])
        compressed.append(run[1])
    return compressed

def run_length_decoding(compressed, shape):
    # 将压缩的游程序列进行解码
    runs = []
    for i in range(0, len(compressed), 2):
        runs.append([compressed[i], compressed[i+1]])
    # 还原原始图像
    pixels = np.zeros(shape).flatten()
    pos = 0
    for run in runs:
        for i in range(run[1]):
            pixels[pos] = run[0]
            pos += 1
    return pixels.reshape(shape)

def compress_image(image):
    # 将彩色图像的 RGB 三个通道分别进行压缩，并将三个压缩后的游程序列合并
    r = run_length_encoding(image[:,:,0])
    g = run_length_encoding(image[:,:,1])
    b = run_length_encoding(image[:,:,2])
    return np.array(r + g + b)

def decompress_image(compressed, shape):
    # 将压缩后的游程序列分别解码，并将三个通道合并成彩色图像
    r_size = shape[0] * shape[1]
    g_size = shape[0] * shape[1]
    b_size = shape[0] * shape[1]
    r = run_length_decoding(compressed[:r_size*2], shape[:2] + (1,))
    g = run_length_decoding(compressed[r_size*2:(r_size+g_size)*2], shape[:2] + (1,))
    b = run_length_decoding(compressed[(r_size+g_size)*2:], shape[:2] + (1,))
    return np.concatenate((r, g, b), axis=1).reshape(shape)

# 测试
image = np.random.randint(0, 256, size=(100, 100, 3)).astype(np.uint8)
compressed = compress_image(image)
decoded = decompress_image(compressed, image.shape)
assert np.allclose(image, decoded)

这个算法对彩色图像进行了压缩，但是压缩率可能不如对灰度图像进行压缩。所以在实际应用中，需要根据具体情况选择使用哪种压缩算法。