将真彩色 RGB 图像转换为索引颜色
我想使用索引调色板将 24 位 RGB 图像（每个通道 8 位）转换为 8 位。
我最初的想法是创建一个数组并简单地计算每种颜色在图像中出现的次数，但我认为如果有大面积的颜色略有变化而占用了所有调色板空间，那将是浪费较小但可能更重要的颜色组。
完成调色板的构建后，我的想法是将每种 RGB 颜色视为 3 维矩阵，并将其点积与调色板中的每个条目进行比较。
...
正如您可能会看到的，我并不完全了解术语，但我希望您明白我的意思:)
我的问题是；有谁能够分享关于如何解决这个问题的见解，或者也许能让我找到在线阅读材料的正确方向？
谢谢！
I want to convert a 24bit RGB image (8 bit for each channel) 8 bit using an indexed color palette.
My initial idea was to create an array and simply count the amount of times each color was represented in the image, but I figured it would be wasteful if there were large areas with slight change in color that used up all of the palette space in favor of smaller, but maybe more significant color groups.
Once I complete building the palette, my idea was to consider each RGB color as a 3-dimensional matrix and compare its dot product with each entry in the palette.
...
As you might see, I'm not completely in on the terminology, but I hope you get what I mean :)
My question is; Is anyone able to share insights on how to approach this or perhaps put me in the right direction to any reading material online?
thanks!
如果你对这篇内容有疑问，欢迎到本站社区发帖提问 参与讨论，获取更多帮助，或者扫码二维码加入 Web 技术交流群。
绑定邮箱获取回复消息
由于您还没有绑定你的真实邮箱，如果其他用户或者作者回复了您的评论，将不能在第一时间通知您！
发布评论
评论（2）
根据 Paul Heckbert 1982 年的论文 流行度算法不如 Median Cut。
有一系列类似 Median-Cut（空间细分）的算法选择不同的标准，例如 最小化方差每个分区中的颜色的）。
使用八叉树进行快速但丑陋的细分。
有 K-Means 和 Linde-Buzo-Gray 等聚类算法。
一个有趣的奇怪的网络是 NeuQuant 神经网络。
我仍在尝试找出 pngquant 的最佳选择。
According to Paul Heckbert's paper from 1982 popularity algorithm is inferior to Median Cut.
There's family of Median-Cut like (space subdivision) algorithms that choose different criteria, e.g. minimize variance of colors in each partition).
There's fast, but ugly subdivision using Octtree.
There are clustering algorithms such as K-Means and Linde-Buzo-Gray.
An interesting odd one is NeuQuant neural network.
I'm still trying to figure out the best one for pngquant.
您正在寻找颜色量化。
You're looking for color quantization.