Image courtesy of Philip Bloom. Click through for his actual coverage of the show.

Canon Expo kicked off with a bang as the venerable imaging giant stunned crowds with its working prototype 4K camera! Is this the future of digital cinema?

Ug. I think I just threw up in my mouth a little.

There’s so much wrong with this prototype “concept camera” (as Philip kindly points out below, it’s important to understand that this is not a camera Canon plans on bringing to market) that I hardly know where to begin. It’s an atrocity of aesthetics and ergonomics. It has a fixed, not-very-special 20x zoom lens. The sensor is only 2/3”. It shoots 60fps. Nothing about this camera reflects any awareness of what digital cinematographers want. It’s as if Canon brass lifted the internet ban on the engineer’s dungeon just long enough for them to visit to RED’s web site, and then shut it down again after they’d read as far as “4K.”

Of course, this isn’t a camera, or even a plan for a camera. It’s a statement by Canon. They meant to say “We get it. We know what’s important.” What they actually said is “Ooh, bigger numbers!” I expect this kind of technology dick-swinging from Sony, but not from Canon.

What’s most troubling about this non-camera is that Canon made it at all. Anyone invested in Canon’s gear should be pissed at Canon for squandering their time and resources building this toy. I look at this thing and see my parents returning from Vegas with a sheepish expression on their faces, saying “Remember your college fund?”

Canon, please stop building fake “4K” video cameras when you can’t even make an SLR that shoots actual 1080p HD.

We’ve discussed the very real aliasing/moiré issue with Canon’s HDSLRs. It is both a real problem and a somewhat workable limitation that many happily accept. What is not in dispute is that these are symptoms of a poorly-sampled, low-resolution image. Readers of this site know that I am not a spatial resolution fetishist, but even I am painfully aware that my 5D and 7D footage is lacking detail.

Canon makes it incredibly easy to demonstrate this, since the 5D Mark II that makes such fuzzy pseudo-HD video also makes ridiculously high-fidelity stills. The frames below are a 1:1 frame grab from a 5D video, and the same scene shot as a still and then scaled down to 1920x1080. I did the scaling in Lightroom, using no Develop or Output Sharpening.

Click for full-sizeClick for full-sizeFeel free to download the full-res originals and compare yourself, or look at the 1:1 comparisons below:

Port of Oakland? Or #### ## #######?Chain-link fence? Or a dirt piece of glass?The difference is staggering. And remember, I don’t even care all that much about spatial resolution. What I do know is this: a sharp 1920x1080 camera can make an image with more detail in it than most female movie stars are comfortable with for close-ups. So for today we can dispense with a discussion about the merits of mastering at 4K. Canon is nowhere near that conversation with their HDSLRs. They’re still falling way short of HD.

To be clear: What you are seeing above is two different ways that the same camera made a 1920x1080 image. One image was hastily yanked off a sensor (skipping entire rows of pixels) and then compressed to H.264 in realtime; the other was captured as raw 5.6K bayer data, decoded slowly to RGB by an engine optimized for quality, then downsampled to HD using every 5.6K pixel to build a 1920x1080 image with as much detail as appropriate for a true HD image.

Does the latter sound unfeasible for “real” video work? Well it shouldn’t — it’s what we do with our RED One cameras now.

The RED One is more than just a “4K camera.” It’s a 4K sensor, some very clever software, an even more clever compressed raw codec, and then more clever software. Not to mention a decent form factor, decent colorimetry, smart proxy workflows for editorial, and viewfinders that actually help you expose and focus. It’s a full-fledged 4K ecosystem.

And look how painful it was for RED to get all that working.

Canon, you have none of that stuff, you have no idea how to make it, and you don’t even know that you don’t know this.

So stop dicking around with your fake 4K toys and start making cameras we can use. That we want to use.

Build a full-frame DSLR that shoots high-quality HD video at a variety of frame rates and the world is yours.

I’m terrified that you won’t though — because then you’d have to put it on a pedestal at a trade show and say “Look, we finally built a camera that actually does what we claim our current cameras do.”

So much more fun to say “Hey look, 4K!”

“We so get it.”

Which would you rather have for lunch — something a good friend ordered for you, or the most popular lunch among people who also enjoyed such hits as Turkey Sandwich and Fish Taco?

A few years back, Netflix made headlines by offering a prize of one million US dollars to anyone who could build a better recommendation engine for their online movie rental site. Crazy? Not according to Netflix. Their assertion was that if they could get just ten percent better at recommending movies based on users’ ratings (1 to 5 stars) of previously-viewed films, their revenues would increase by much more than a paltry $1M.

What has always boggled my mind about this challenge is that it’s a classic case of struggling to find a technological solution to a distinctly human problem. Google labors to perfect computer algorithms that convert recorded speech to text. For all their research and computational might, they do a pretty poor job. Meanwhile, a small, smart company called CastingWords* uses Amazon’s “Mechanical Turk” service to assign transcription tasks to hundreds of eager, human laborers who work for pennies. The results are near-perfect.

Netflix is sitting on a nearly Wikipedia-sized repository of user-generated movie reviews. These reviews a not just free to Netflix — since only active members can contribute them, people are actually paying for the privilege of reviewing films on Netflix’s site.

Netflix not only ignores these reviews in recommending movies, it also ignores your reaction to the reviews. This is the 100% human answer to their “technological” problem that has been staring them right in the face for years.

When I give Face/Off five stars, am I doing it because I love John Travolta Movies? Or Nick Cage flicks? Or John Woo films? Or hyper-violent 90s action? Or any film that features a speedboat chase? Netflix has no idea why I like or dislike a movie, so how can they predict what I might like or dislike next?

When I read the Netflix reviews of Face/Off, two things are abundantly clear: First, many people like that movie for reasons I don’t agree with. Second, people who don’t like Face/Off, with a few notable exceptions, are people whose cinematic opinions I can live without.

In Real Life I have friends who love Face/Off and friends who hate it. And crazily enough, I respect all of their opinions. Any of these friends are welcome to recommend movies to me, and I will almost always take those recommendations.

That’s Netflix’s second mistake: Thinking that we always only want to watch movies that we’d rate highly. I don’t know about you, but I watch plenty of movies that I know won’t be five-star favorites. A friend’s strong endorsement is often the reason — even that guy who hates Face/Off.

But let’s get back to Netflix’s first mistake: Thinking that what everyone thinks matters. I’m sorry, but everyone is an idiot. Even with the challenge complete and the fancy new algorithm implemented, everyone seems to think that because I liked Mission Impossible III, that I’ll jump for anything starring Tom Cruize. Or that liking the first four Steven Seagal films has anything to do with one’s opinions of his subsequent works.

Read some Netflix reviews. While a few are insightful, most are utter garbage.

If a person’s review is garbage, then what good is their star rating? None whatsoever. So the majority of the data used by these million-dollar algorithms is worse than worthless. No wonder the results plateau despite endless efforts.

Of course, your garbage might be my delicacy. On Netflix, you can rate reviews Helpful or Unhelpful. But Netflix obstinately sticks to it’s “everyone’s opinion” philosophy and uses these ratings to bubble Helpful reviews to the top of the list.

The list. No matter how you rate the reviews, you see the same list as everyone else.

You can probably imagine what I’m going to say next. Unless, of course, you work for Netflix.**

If I read a compelling, insightful review on Netflix, and I mark that review Helpful, then factor that person’s ratings higher in determining what films I might like. Similarly, if I mark a review as Unhelpful, then don’t let that fool’s opinions influence my recommendations.

Let me build a personal network of friends whose opinions I respect, and let their recommendations populate my personalized Netflix experience. It will immediately become obvious that a few, cultivated opinions are worth much more that a watered down average of millions.

Oh, and speaking of millions, you can keep the check Netflix. Better recommendations will be reward enough. In case you hadn’t noticed, I’m hooked on you like strawberry crack.

—

* I found out about CastingWords in Seth Godin’s amazing book Linchpin. If it doesn’t make you want to quit your job, then maybe this will.

** In which case you might sponsor a contest to develop a robot that can predict what I’m about to say based on what everyone else has said.

Red Giant just pushed out the last of my first series of Colorista II tutorials, so I thought I’c collect them all in one handy place for you. Probably best to watch them full-screen. Click through the text links to get iPad/iPhone-friendly versions.

01 - Getting Started with Magic Bullet Colorista II from Red Giant Software on Vimeo.

02 - Multishot Workflow With Magic Bullet Colorista II from Red Giant Software on Vimeo.

03 - Working with The Keyer in Magic Bullet Colorista II from Red Giant Software on Vimeo.

04 - Working with Power Masks in Colorista II from Red Giant Software on Vimeo.

05 - Cosmetics: Skin Retouching with Magic Bullet Colorista II from Red Giant Software on Vimeo.

06 - Highlight Recovery With Magic Bullet Colorista from Red Giant Software on Vimeo.

Photo by Diana Stock using iPhone 4 and Plastic Bullet

You asked, we listened.

Plastic Bullet 1.1 (US$1.99, free update for current users) is available now, featuring some big new features pulled directly from your requests.

• Full-res photo developing on every iPhone
• Share your photos using Flickr and Facebook
• Faster photo developing
• Optimized for iOS4 and the iPhone 4

Read more about the new features in iTunes. You can see some full-res iPhone 4 examples on my Flickr feed — the quality is amazing.

Enjoy!

Kudos to Celtx for figuring out exactly what to include and what to leave out when designing a screenwriting app for mobile devices. Celtx Script (US$9.99) is the first iPad screenwriting app that “just works” in the way that Apple users expect. This is a welcome surprise given how clunky and homely the desktop Celtx application is on OS X.

Celtx Script on the iPad is as simple and elegant as one would hope. You can do most of what you need to just by typing. In portrait view you have a distraction-free view of your script. In landscape, there’s a handy scene list to the right for navigation.

Landscape view has a centering problem where the last character is cut off on the right. I trust that this is an easy bug to fix.

A feature I would love for both the iPad and the desktop version is folders in the scene list. Color-coding scenes would also be nice.

Notice how I praise the app for its minimalism and then request new features. See how difficult life is for developers?

Speaking of which, the developers were caught by surprise with Celtx Script hitting the App Store on a Saturday, so while the free Celtx Sync function would work between an iPhone and an iPad running Celtx Script, there was no way to sync between the free desktop Celtx and your mobile device. One of the developers managed to get the free syncing plug-in posted within a few hours though. Once you get the plug-in installed, you can import a screenplay from the free cloud backup using desktop Celtx’s Script > Import Script > From iPhone/iPad menu item. There’s a corresponding Export option as well. It’s not quite the same thing as a true Google Docs-style cloud sync, but it’s close, and it’s free.

I would love some assurance from the Celtx team about the security of the cloud storage.

I’m delighted that someone finally made a solid and elegant screenwriting solution for the iPad. That it works on the iPhone as well and syncs with free desktop software makes the $10 price a bargain.

Many folks asked why I ignored Celtx in my last screenwriting post. My answer was that it didn’t provide any features missing from my various other tools. That just changed in a big way.

Looking for screenwriting resources? You can follow my screenwriting list on Twitter, and check out my recommended screenwriting books at the ProLost Store.