Ah, Blade Runner. You are one of the good guys.

No, I didn't get angry (and no, I am not a Replicant). It actually saddened me that what seemed like a useful conversation stopped dead-cold with such a personal comment. As I said, I have devoted a lot of my life to image and color processing. I guess it's part of the problem of being somewhat anonymous, something I am moving away from slowly.

Look, it's easy to come off as arrogant over email, newsgroups or similar means of communications. Part of it is that sometimes people take it the wrong way when someone comes out in an authoritative manner. I do. However, I only do that when (a) I really, really know what the hell I am talking about and (b) I don't have the time to write twice as much text to cover all corner cases and be sure that everyone sees me as "nice". I've heard talks by Linus where I've thought he came off as an arrogant asshole. Then I slowed down and realized where he was coming from. Once you understand that it all makes far more sense and, yes, it stops feeling arrogant.

I'm not 16 any more, so I don't really care about seeming "nice" online because, well, it's hard and it takes time. This, for me, isn't a popularity contest. I'm simply, honestly, trying to share something and learn as well. For example, I don't use Python that much at all. Inspired by this thread I sat down and played with Python quite a bit. That's a good outcome, at least for me anyway.

With regards to the idea of favoring green more than red and blue. This isn't the intent of the equations. This is actually what happens in the real world. If you look at the spectral power distribution of a captured image you will see that, generally speaking, there's a lot more energy around the green portion of the spectrum. I am over-simplifying and cutting corners here, but that's one way to think of it.

In other words, in normal images with normal lighting there's far more green stuff than red or blue. And so, in converting an image to a grayscale representation you have to account for the fact that green contributes to the image twice as much as red and six times as much as the blue component. If you don't apply these weights to the image you are going to be evaluating such things as noise and attributing far more value to image structures in the other channels.

Another generalization is that image noise is generally found in the blue channel far more prominently than the other channels. If you simply average all three channels you are effectively amplifying the blue channel. Blue should have had a weight of about 10% and you are giving it 33%. You have just tripled it's importance and, if there's any noise there you've just multiplied it by three. When it comes to green, you are halving it's contribution from about 60% to 33%. Here's the component that generally contributes the most information to an image and, by averaging it with the other colors, its contribution is now cut in half. Finally, red is the component that suffers the least (almost not at all) from averaging. Red contributes about 30% to an image; averaging amplifies it to 33%.

With regards to a blog. Actually, I've been thinking about it. Maybe later this year. A blog feels far more "serious" than posting in places like HN.

Don't feel bad either. Life is too short to get worked up about stuff that, in the grand scheme of things, matters not at all.