算法与计算机科学alvyray.com编辑精选
像素不是小方块（1995）[PDF]
A Pixel Is Not A Little Square (1995) [pdf]
2014 — 2021，它一次次回到首页
黑点是上首页（≥ 50 分）的那次投稿，空心点是没冲上去的投稿，点越大分数越高。
导读
这是计算机图形学先驱 Alvy Ray Smith 于 1995 年撰写的经典备忘录，论证“像素不是一个小方块”，而是基于采样定理的点采样，需要用重建滤波器才能还原连续图像。文章多次登上 HN 首页，是图形学领域绕不开的基础读物。
为什么经久不衰：它纠正了一个流传至今的行业误解，且随着显示技术演进，关于像素本质与重建滤波的讨论至今仍在继续，值得每个图形/图像开发者重读。
HN 评论精选
像素重建与 gamma 的核心问题无法靠理论争论解决，需要基于现代显示技术的用户研究
我想这篇最近被翻出来，部分是因为之前的 alpha 合成讨论。我们急需一些基于用户研究和现代显示技术的实验，来回答一些基本问题：什么重建滤波器效果最好？对矢量（文字）和自然图像是否相同？渲染极细线条（文字相关）时，什么样的 gamma 曲线能让不同亚像素相位下的线条看起来粗细一致？黑底白字与白底黑字的细线等宽又需要什么 gamma 曲线？很多人喜欢从第一性原理出发争论这些问题，但“正确”的做法往往不如把像素当小方块、忽略 gamma 的常见假设好看。我认为这些问题无法靠争论解决，只能靠用户研究。
raphlinus查看英文原文
I imagine this came up partly as a result of the recent alpha compositing discussion. We desperately need some research, based in user studies and using modern display technology, to settle some basic questions: * What reconstruction filter gives the best results? Is it the same for vector (text) and natural images? By "best" I do mean contrast (sharpness) and lack of visible artifacts. * For rendering of very thin lines (relevant to text), what gamma curve gives the perception of equal line thickness across the range of subpixel phase? How does it vary with display dpi? (Hint: it's likely not linear luminance) * What gamma curve yields the perception of equal width of black-on-white and white-on-black thin lines (also relevant for text)? (Hint: likely not linear luminance) I've seen a number of discussions where people feel they are able to answer these questions from first principles, and corresponding arguments that doing these in a "correct" way gives results that are less visually appealing than the common assumptions of treating a pixel as a little square (so doing a box filter for reconstruction) and ignoring gamma so effectively using a perceptual color space for the purposes of alpha compositing. I posit that these questions cannot be solved by argumentation. I think user studies might not be very difficult to do; you could probably get "good enough" results by doing online surveys, though this wouldn't pass standards of academic rigor.
实用主义反驳：把像素当小方块在工程上更有用，教学上也应如此
像素就是图像元素，名字本身就说明了这一点。事实证明，把它们看成排列在矩形网格上的小方块非常有用，因为计算机就是这样处理它们的，而不是当作点采样。这篇文章让我想起许多数学教材坚称“向量不是数字元组”——技术上或许没错，但把向量当数字来算实用得多。我教图形编程时，还是会告诉学生像素就像小方块。
bjourne查看英文原文
A pixel is a picture element. An element of a picture. Hence the name... It turns out that thinking of them as little boxes arranged in rectangular grids is very useful. Because that is how computers deal with them. Not as point samples. The article reminds me of the many mathematical text I've read insisting on that vectors are not tuples of numbers. That thinking of them as anything other than directions with magnitudes is wrong. Technically, that might be correct but vectors-as-numbers is much more useful when calculating with them. When you get into more abstract mathematics, and your vectors contain other kinds of algebraic objects, such as polynomials, you are already so accustomed with them that you can think of them as flying burritos if you like. When I teach graphics programming, I will continue to tell students that pixels are like little boxes.
时代背景提醒：LCD 时代像素确实更接近方块，重建问题与 1995 年已不同
这篇是图形学人的经典必读，但别忘了它写于 LCD 出现之前。如今的像素在更大程度上真的是小方块了（相比 1995 年的 CRT）。这不改变论文的理论正确性，但意味着“完美重建”的标准已经和当年不同。
dahart查看英文原文
This piece is a classic and a must-read for graphics people, but do remember that this was written before LCD displays. Today’s pixels actually are little squares to a much greater degree than CRTs in 1995. That doesn’t change the theory or truth in Alvy Ray’s paper, but it does mean that the perfect reconstruction isn’t the same now that it was then.
提供作者背景与阅读提示：理解本文需要采样定理知识
补充一点背景：作者是 Alvy Ray Smith，计算机图形学的先驱之一。正如他在引言中所说：“如果你发现自己认为像素是一个小方块，请读读这篇文章。”要从“点”的理解过渡到“方块”，需要往后读一点：采样定理告诉我们，用合适的重建滤波器可以从离散实体重建出连续实体。建议配合奈奎斯特-香农采样定理的维基百科条目一起阅读。
fsloth查看英文原文
A little bit of background for the paper and a brief summary might be beneficial: The author is Alvy Ray Smith. He's one of the pioneers of computer graphics (http://en.wikipedia.org1/wiki/Alvy_Ray_Smith). As the author states in the introduction: "If you find yourself thinking that a pixel is a little square, please read this paper." To get from the point to the squares -interpretation you need to read a little bit forwards: "Sampling Theorem tells us that we can reconstruct a continuous entity from such a discrete entity using an appropriate reconstruction filter" This might be proper background reading before or after reading the paper: http://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem
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