In a recent Terence and Philip Show we wondered whether outsourcing or automation would kill us first. Now we have an example of a music video being completely outsourced, with apparently great results.

Derryl Murphy sez, “Drew Smith‘s lovely new song ‘Smoke and Mirrors’ needed a video, so he decided to outsource it. The result is wonderful.”

So I outsourced my video to Bangalore, India. Why? Well, I figured the last thing the world needed was another low-budget singer songwriter video. Fortunately, the first Virtual Assistant I found on google also happened to be a dance choreographer. After a couple of emails and phone calls, I received this beautiful video in my inbox. Many thanks to Asha Sarella and Vishwas Avathi. I can’t thank you enough!

• Bug fix for DV 720×576 sequence settings
• Bug fix for 44.1 kHz audio settings
• Bug fix for 0% Speed Rate
• Bug fix for markers on Generators
• Bug fix for non-standard frame rates.

I feel particularly chagrined about the PAL related bug. Having lived in a PAL country (Australia) for most of my career, I was often on the receiving end of less-than-complete PAL support compared with the NTSC. Some may recall that FCP 1 did not officially support PAL at all. It didn’t stop me but it wasn’t officially supported.

These bugs were a surprise – particularly the 44.1 KHz one – as 7toX for Final Cut Pro has been the most-tested of any of our apps. It is, as I said, also the most complex by several orders of magnitude.

It takes a couple of days to get through the App Store update process, so I’d estimate the update being available by Feb 6 or 7.

We apologize to everyone who’s been bitten by these bugs.

Note: If ever you run into a problem translation, please send the XML file to info @ IntelligentAssistance.com (without the spaces). This helps us track down the problem and fix it.

7toX for Final Cut Pro

Media needs to be online during export from FCP 7 (or 6); during the conversion process (as 7toX checks details from the media) and during import to FCP X.

7toX for Final Cut Pro is expected to work with FCP 6 XML, but it is untested.

Unfortunately Final Cut Express does not export XML so we are unable to update Final Cut Express Projects.

Video Output

Video Output requires new drivers for your hardware. As of today only AJA have a beta of their FCP X output drivers available. Blackmagic Design and Matrox are expected to have betas of their drivers shortly. It’s important to note that these drivers are the first ever to be written to the new CoreMediaIO services on OS X. Previous drivers have all been QuickTime based, so this is all new work, not a simple update to drivers as previous versions were. That’s also why the video output is Lion only: only Lion has the foundations in place for the Core services functions needed, and work couldn’t start on the drivers until Lion was out, and a beta version of FCP X was available for hardware companies to work with.

As expected there is support for broadcast video out via third party hardware, although that feature is currently “in beta”: it works but not as well as Apple and the hardware folk would like. It’s available for those who need to use it. One less reason to not use Final Cut Pro X.

Multicam in Final Cut Pro 7 was definitely a tool for those who knew what they were doing. In Final Cut Pro X multicam is super simple: mix frame sizes, codecs and frame rates in different angles. I love that we can open the multiclip group into a Timeline and simply drag angles up or down to change their order; or to add filters to one or more angles; or add (or remove) angles from the multicam group without needing to remake the group. Another nice feature is to add in an audio only “angle” and tell Final Cut Pro X to use the audio from that angle, without switching audio.

Multicam, like so much of Final Cut Pro X, is a rethink that considered what would make multicam more accessible to more people by eliminating the “hard parts”: let the software adapt rather than forcing a certain level of knowledge on users. (Sorry for those who had this somewhat hard earned knowledge, but that is the way things move.) For example, the default way of making a multicam group is to base it on the audio waveforms. (Timecode, start points and other options are available.)

Other terrific new features not pre-announced are support for layered Photoshop files and reconnect media, which work pretty much as you’d expect. Also unexpected is an Advanced Chroma Keyer with more controls available. The basic key is still pretty darn good, but having advanced tools for cleaning up is valuable for those tricker keys.

Oh, and one other thing: Intelligent Assistance (that’s us) have released 7toX for Final Cut Pro. Bring your Final Cut Pro 7 projects forward to Final Cut Pro X. For the story, check out my post on 7toX for Final Cut Pro.

We have broadcast video out for those who need it, still in beta because it’s now based on the new CoreMediaIO instead of QuickTime and this is the first time AJA, Blackmagic Design or Matrox have ever worked with these Core Services, so it’s reasonable to cut them some slack. AJA have drivers ready today, with the others following up shortly. It must be hard for those guys having to constantly keep up with new versions of drivers for Avid’s Media Composer 6 and presumably some new version of Premiere Pro since Al Mooney strongly implied we would hear more about the next version of the Creative Suite “around NAB”, extrapolating from the previous two releases.

We have a great implementation of multicam. We have layered Photoshop support and reconnect media, and we have a way of moving Projects forward. That seems to have addressed most of the “deal breakers” people have had. Bottom line now is that we’re at the point where it’s now possible to evaluate Final Cut Pro X on its own merits: not how similar or not it is to other applications, but as a viable alternative editing interface.

I don’t expect that everyone will suddenly jump in and love Final Cut Pro X: if you really don’t find the Magnetic Timeline “works” for you then Final Cut Pro X probably isn’t going to be for you. That’s fine, I like the idea of choices in interface as it’s not something we’ve had before: Final Cut Pro 1-7, Premiere Pro, Media Composer all were based on the same metaphors (and in fact two of the three originated from the same mind.)

I definitely still have features I’d like to see in Final Cut Pro X – at the top for me is selective copy and paste attributes – but now that the major elements are in place, I’m sure we’ll see those features getting some love in the future: by implication we’re likely to see a couple more releases this year.

So, I say, love it or not (and I love it enough to plan on using it for a complex reality show under tight conditions this year) Final Cut Pro X is ready for prime time again, while acknowledging that it may not be for everyone. But now we have good viable choices, something we didn’t have before. I also believe we have a faster alternative that suits my edit style, but that remains to be quantified.

Yes, it works. It does an amazingly good job of translating Final Cut Pro 7 Projects (Bins and Sequences) into a Final Cut Pro Event. Clips from a FCP 7 project become clips in FCP X, Bins become Keyword Collections and Sequences are translated to Compound Clips, which can be:

• opened directly in a Timeline; a
• added to a new Project as a Compound Clip;
• broken apart to make a FCP X Project.

It is true to say that it’s the highest fidelity translation possible; it’s simple to use and it’s only $9.99 in the Mac App Store.

So what happened between September 22nd – when I wrote all the reasons why we weren’t making a 7 to X product – and now when we’re releasing one? As I wrote then, I always had the impression someone was working on it: either Automatic Duck or Apple. But it all went quiet. So, if no-one else was working on it, then we sure would like to. We knew from Xto7 for Final Cut Pro that translation was possible. (Turns out it’s harder going forward than back.)

So we approached Apple to find out if anyone else was working on it, expressing our interest in making it happen.Unlike almost any other Final Cut Pro X development, converting the XML seemed like a good third party opportunity. It turns out the opportunity was open, and Apple were very happy to work with us because of our understanding of XML. Of course it’s Greg Clarke who deeply, deeply understands the FCP 7 xmeml format and had already more experience than almost anyone else with the new FCP X fcpxml format. Apple graciously gave us both their blessing and early access to the 10.0.3 release, so we targeted that for the translation tool.

I’ve often said, in the last few months, that I would not want to be the person developing a Final Cut Pro 7 to X translation tool. I was telling the truth because I could hear the salty language that came from Greg’s work area: there’s no sugar coating it, this is the most complex piece of software we’ve ever written, and yet it’s the simplest to use. (Drop FCP 7 XML on app icon. Done.)

Along the way I learnt a whole lot more about Final Cut Pro 7 as I translated the video functionality from app to app. (Greg then translated that into computer code.) There are things in FCP 7 I never knew existed. There was at least one missed app opportunity! I also learnt that, indeed, these were two very different applications, with different data structures and two very different design mindsets. Translating from one to the other is difficult, in part because the new app was designed without thought for backward compatibility, which is entirely reasonable. Focusing on backward compatibility would bind the new app with legacy thinking.

I’m very proud of what Greg and I have achieved because the finished result is so much better than we ever thought it could be. I knew we could make a useful translation – definitely better than “cuts only” – but how high we could make the fidelity between the two versions, I just didn’t know. There are some things lost in translation, as a perfect translation isn’t possible between two very different languages. (Imagine translating Spanish into Mandarin, when you natively speak English and you’ll get an idea.)

You can find a full list of how the translation works at the Assisted Editing site, but some highlights for me are:

• Automatically converting PICT files to high resolution TIFF files because FCP X does not support PICT files.
• The idea of using the Timeline Index’s To-do markers to report any substitution from the original FCP 7 Project.
• Using Compound Clips for Sequences creates an Event structure that is very similar to FCP 7′s Project. Each “Sequence” becomes a Compound Clip in the FCP X Event, grouped in a Keyword collection “FCP 7 Sequences”.
• Using Roles to report the original track numbers: this is metadata and I would not allow it to be lost.
• Translating, as best as possible, the intention of the track usage in FCP 7 into the Magnetic Timeline required some intelligent interpretation of the original track structure into a FCP X context.
• That we do not lose any log notes, even though they don’t map well to Final Cut Pro X’s data structures. They’re all there in the Notes field and searchable in FCP X.
• We fully support translation of multi-cam projects from FCP 7 to FCP X.

There’s a lot of other great stuff that it does: audio and video filter substitutions; transition substitutions; all Motion Tab settings are translated, and so on. About the only caveat is that the media must be online throughout the export, translation and import to Final Cut Pro X process.

If you’re moving from Final Cut Pro 7 to Final Cut Pro X then 7toX for Final Cut Pro is going to be your new best friend. Even if you’re just feeling your way, 7toX for Final Cut Pro will also work with the free trial version of Final Cut Pro X so you can check out how closely translated your Project will be before upgrading.

Oh, and why is it so much cheaper than Xto7 for Final Cut Pro? Because we wanted to make buying it an easy decision while balancing out the enormous amount of effort that went into it (seriously, it’s dominated the last 6 months of our lives).

Actually this is no surprise. The traditional players – RIAA and MPAA – keep complaining about how their “industry” is shrinking, but in fact:

This all points to the fact that what is happening within the industry is not a challenge of a business getting smaller — quite the opposite. It’s about the challenge of an industry getting larger, but doing so in ways that route around the existing structures.

The industry is getting bigger, but recorded music on round discs isn’t. The film and TV business is getting bigger but not through the MPAA’s member studios. Remember these are lobby groups for legacy businesses, not representative of the modern industry.

This is a great study.

Some of the key points:

• Entertainment spending as a function of income went up by 15% from 2000 to 2008
• Employment in the entertainment sector grew by 20% — with indie artists seeing 43% growth.
• The overall entertainment industry grew 66% from 1998 to 2010.
• The amount of content being produced in music, movies, books and video games is growing at an incredible pace.

Read it for the conclusions and methodology and continue to resist the dinosaurs approach to protection: they don’t need SOPA/PIPA/ACTA or any of the other attempts to kill innovation to protect legacy business that do not represent any creative artist.

Firstly, the distinction between facial detection and facial recognition is the difference between recognizing “oh, that’s a face” compared with “oh that’s Philip Hodgetts’ face”. That’s a huge distinction. Most digital still cameras sold today recognize faces in the image and attempt to lock focus on them. Heck, Smile Click recognizes that a subject is smiling before taking a picture, and that’s a 99c app! Facial detection is a developer framework in iOS 5, making it easy to add facial detection to any app – for tracking, changing or modifying! Sanyo have facial detection (they call if Face Chaser) in a video camera. In professional cameras, Fujifilm have released a lens with its TRACE technology that tracks faces in shot to maintain focus and exposure – up to 12 faces in a shot.

In postproduction software both Premiere Pro CS 5 and later and Final Cut Pro X attempt facial detection. Neither attempts to identify the individual, just that there is one or more people in the image. Subjectively, it’s still a work in progress. When it works, it’s great but even facial detection isn’t perfect with moving video. Final Cut Pro X further takes this useful metadata (how many people are in a picture) and attempts to infer the shot based on the size of the detected faces: big faces = closeup; many small faces = wide. (I’m sure that’s a gross simplification of someone’s very hard work, but you get the point.)

Facial recognition is valuable because it allows us to quickly group shots with the same person (or character) in them without any additional work. Taking the boring out of post, as I like to say. We’d have to identify the face once and probably deal with some false identifications, but if it gets more accurate than iPhoto currently is, it could be more useful.

I’d also suggest that it’s likely to be more useful in a video project, and more accurate, than in a general iPhoto library because of the more limited set of examples for each person. iPhoto (pre-Polar Rose technology) does a good job where it’s presented with a set of images of the same person in roughly the same time. The more examples in photos across a lifetime, and the accuracy is reduced. So, with the more limited time scale of the typical video project, I’d expect better accuracy.

Facial recognition is still very much a work in progress. In September 2010 Apple purchased Swedish facial recognition company Polar Rose, presumably to boost the facial recognition technology in iPhoto and across their entire product range (I hope!). Full facial recognition probably won’t make it to Final Cut Pro X this year, but you do have to wonder what they have planned for a “full revision” release (one we might pay for) after all the catch-up features are added. (I have absolutely no ideas, this is pure hypothesis/wishful thinking on my part.)

However, if we broaden out a little and consider the research that’s being done – the existing integration of facial recognition in social media and photo sharing sites, and how object detection/computer understanding of what it’s seeing is developing – then it’s obvious that computer recognized metadata will start to be a viable alternative.

Facial recognition

I mentioned Polar Rose, now integrated into Apple where I’d expect it will be used to improve the accuracy of facial recognition in iPhoto and Aperture, but also likely to be added to the facial detection framework now in iOS 5. As iOS and OS X are merging, I would expect the same frameworks to become available to OS X developers in due course. From GigOm - Apple’s iOS facial recognition could lead to Kinect-like interaction:

The unearthed APIs are described as “highly sophisticated,” and can determine where a user’s mouth, and left and right eyes are located, as well as process images taken by the iPhone for face detection. Aside from providing Apple an easy way to introduce Faces (which recognizes specific people in iPhoto) to both its own Photos app and any third-party apps that access that library, it should also open the door for much more advanced facial recognition applications.

Beyond Apple, facial recognition is becoming ubiquitous, even to the point where Slate’s Farhad Manjoo wrote in his “Smile, You’re on everyone’s camera“:

Soon, face recognition will be ubiquitous. While the police may promise to tread lightly, the technology is likely to become so good, so quickly that officers will find themselves reaching for their cameras in all kinds of situations. The police will still likely use traditional ID technologies like fingerprinting—or even iris scanning—as these are generally more accurate than face-scanning, but face-scanning has an obvious advantage over fingerprints: It works from far away. Bunch of guys loitering on the corner? Scantily clad woman hanging around that run-down motel? Two dudes who look like they’re smoking a funny-looking cigarette? Why not snap them all just to make sure they’re on the up-and-up?

This is absolutely a technology who’s time is coming. Further in the Slate article:

In 2006, Google acquired the biometric recognition company Neven Vision, and Hartmut Neven, one of the world’s experts in computer vision, is a respected engineer at the company. A Microsoft research team in Israel has built a fantastic app that uses face-recognition systems to search the Web for pictures of people who are in your photo album. And last year Facebook rolled out a tool that automatically suggests names of people to tag in your pictures.

The Google acquisition has now rolled out as facial recognition in Picassa.

Late 2011 the New York Times had a feature article Face Recognition Makes the Leap From Sci-Fi in their business section. In the article they list these examples:

• SceneTap, a new app for smart phones, uses cameras with facial detection software to scout bar scenes. Without identifying specific bar patrons, it posts information like the average age of a crowd and the ratio of men to women, helping bar-hoppers decide where to go. More than 50 bars in Chicago participate.
• Immersive Labs, a company in Manhattan, has developed software for digital billboards using cameras to gauge the age range, sex and attention level of a passer-by.
• Those endeavors pale next to the photo-tagging suggestion tool introduced by Facebook this year. When a person uploads photos to the site, the “Tag Suggestions” feature uses facial recognition to identify that user’s friends in those photos and automatically suggests name tags for them. And apparently you don’t need a person’s permission to tag them on Facebook.
• “Millions of people are using it to add hundreds of millions of tags,” says Simon Axten, a Facebook spokesman. Other well-known programs like Picasa, the photo editing software from Google, and third-party apps like PhotoTagger, from face.com, work similarly.

Powered by technology from Face.com (one of the key players in white label face detection) Facebook processed 400 million photographs in 30 days!

So my dream of having all the people in my source video recognized and grouped. Perhaps for manual naming (once) but more likely we’ll be able to use existing resources to match the face somewhere and associate the name with it. In a study reported in the Cloud-Powered Facial Recognition Is Terrifying article at The Atlantic:

Unlike Groucho Marx, unfortunately, the cloud never forgets. That’s the logic behind a new application developed by Carnegie Mellon University’s Heinz College that’s designed to take a photograph of a total stranger and, using the facial recognition software PittPatt, track down their real identity in a matter of minutes. Facial recognition isn’t that new — the rudimentary technology has been around since the late 1960s — but this system is faster, more efficient, and more thorough than any other system ever used. Why? Because it’s powered by the cloud.

….

With Carnegie Mellon’s cloud-centric new mobile app, the process of matching a casual snapshot with a person’s online identity takes less than a minute. Tools like PittPatt and other cloud-based facial recognition services rely on finding publicly available pictures of you online, whether it’s a profile image for social networks like Facebook and Google Plus or from something more official from a company website or a college athletic portrait. In their most recent round of facial recognition studies, researchers at Carnegie Mellon were able to not only match unidentified profile photos from a dating website (where the vast majority of users operate pseudonymously) with positively identified Facebook photos, but also match pedestrians on a North American college campus with their online identities.

At most you need one photograph of the individual, and good matching algorithms. CNET News has a background article that gives another take on the experiment identifying random people in public spaces.

It’s getting quite common:

• FTC Calls For Comments On Facial Recognition Technology
• Ubiquitous facial-recognition software is coming
• FBI rolling out nationwide face search and recognition system
• London Police Use Flickr to Identify Looters

Facial Recognition Technologies

Before moving on to other ways computers are “seeing” images, here’s a short summary of the primary technology providers.

• Google purchased PittPatt and are integrating the technology into Picassa
• Apple purchase Polar Rose and are integrating the technology widely through their OS and applications.
• Facebook relied on Face.com technology. In fact Face.com are one of the major providers of facial recognition technology with 7 Billion Photos in the last year in its Facebook apps, Face.com is now available for developers (such as Facebook, and well, even Assisted Editing) to use:

• Developers who are interested in building their own facial recognition apps can now get full access to the open Face.com API, free of charge. That basically means developers can tap into Face.com’s face detection and recognition technology and create brand new ways for friends to engage through photos at zero cost. Hard to beat that offer.
• It is also well funded: “Yandex, operator of Russia’s largest search engine, has invested in Tel-Aviv based facial recognition technology startup Face.com, marking its first investment in an Israeli company. In total, Face.com has raised$4.3 million in Series B funding in a round led by previous investor Rhodium.”

I want to draw special attention to Affectiva who use the tag line “Respectful emotional measurement and communication.” Yes, they read emotion from video.

The software “makes it possible to measure audience response with a scene-by-scene granularity that the current survey-and-questionnaire approach cannot,” Mr. Hamilton said. A director, he added, could find out, for example, that although audience members liked a movie over all, they did not like two or three scenes. Or he could learn that a particular character did not inspire the intended emotional response.

I fully expect facial recognition to come into the postproduction world rapidly and provide useful metadata. Automatically identifying and labeling people in video content will be very empowering. I do foresee an issue with *automatic* identification with actors and roles in narrative, but the software would surely have the ability to manually tag every instance of “this identified face” with a character name instead of the actor’s name sourced from IMDB!

If software can detect emotional responses to movies, it can detect emotional performances and – for documentary/reality/news – detect the emotion in the face to help drive editing.

Oh, if you really want to avoid facial detection? Social Beat has a few tips.

Metadata for asset management

Both DigitalSmiths and Asterpix have tools specifically intended to create visual metadata automatically for asset management and exploitation.

Asterpix

The actual process of machine-tagging involves pulling in imaging data from the video clip and matching it up to whatever text was included by the video’s creator. Nat Kausik, CEO of Asterpix tells me the process is a little similar to Google’s search algorithm in creating relevancy based on what bits of parts of the video get the most screen time. For example, in a video of a someone walking through a grocery store there would be a wealth of information about other products and people, but if you’re focused on that one person for the majority of the clip the engine will pick on it and react accordingly.

DigitalSmiths

Digitalsmiths is the technology leader in the rapidly growing segment of video search and recommendation. Digitalsmiths Seamless Discovery™ has revolutionized the accuracy and ease with which end-users find relevant, personalized entertainment across multiple channels and devices.

From powering tablet-enabled set-top-boxes, to live integration of sporting events, to first-run premium web-content, Digitalsmiths has deep experience and proven results for increasing engagement and viewership though our unique, holistic solutions. Our solutions serve customers that span across all media channels and devices and reach a combined audience of millions of consumers.

Generating other visual metadata

While we’re only just seeing facial detection and recognition rolling into useful applications, but already computers are being taught to read the text in images, and process the images themselves to recognize people, place and things.

Text recognition isn’t exactly new, but:

Google Goggles Can Now Read Print Ads. Oh, And Play Freaking Sudoku!

OCRKit 1.2 – The simplest Text Recognition for the Mac

Beyond Text

Computers That See You and Keep Watch Over You

A New York Times article outlining how computer vision is being used:

Perched above the prison yard, five cameras tracked the play-acting prisoners, and artificial-intelligence software analyzed the images to recognize faces, gestures and patterns of group behavior. When two groups of inmates moved toward each other, the experimental computer system sent an alert — a text message — to a corrections officer that warned of a potential incident and gave the location.

“Machines will definitely be able to observe us and understand us better,” said Hartmut Neven, a computer scientist and vision expert at Google. “Where that leads is uncertain.”

The applications are quite amazing. From observing prisoners, to reminding hospital staff to wash their hands if it’s detected they haven’t, to a host of other uses, smart computer software is observing us and making accurate observations.

Google Experiments With Next Generation Image Search

Notably, the new image search technology doesn’t just index text associated with an image in determining what’s in it. Google is now talking about using computers to analyze the stuff in photos, and using that to associate it in a ranked way with keyword queries. In effect, they’re talking about something similar to PageRank for images (but without the linking behavior).

Teaching Google To See Images

Nuno Vasconcelos, a professor of electrical engineering at the UCSD Jacobs School of Engineering, discusses the approach, called Supervised Multiclass Labeling (SML), in a recentnews release from the school (hat tip to Threadwatch for the pointer). Though SML sounds like a mouthful of jargon, what it really amounts to is systematically training a computer to recognize statistically similar objects, and teaching it to differentiate them from other objects that have similar characteristics.

Google Researchers Teach Computers Out How To Recognize Images Of Famous Landmarks

In the experiment, the researchers fed “an unnamed, untagged picture of a landmark” found on the Internet and the system would spit back the name and location of the landmark, such as the Acropolis in Greece. Each untagged photo was be compared to 40 million GPS-tagged images on Picasa and Panoramio (both owned by Google), as well as related photos found through Google Image Search. Using clustering and new image indexing techniques, the Google researchers were able to identify untagged photos of the same landmarks from different angles and under various lighting conditions.

The researchers report that their system can identify 50,000 landmarks with 80 percent accuracy. I’m not sure that’s quite good enough to even roll that out in a beta product, but if Google can get it to 90 percent or 95 percent that would start to be consumer-friendly.

Researchers from MIT’s CSAIL teach computers to recognize objects

The system uses a modified version of a so-called motion estimation algorithm, a type of algorithm common in video processing. Since consecutive frames of video usually change very little, data compression schemes often store the unchanging aspects of a scene once, updating only the positions of moving objects. The motion estimation algorithm determines which objects have moved from one frame to the next. In a video, that’s usually fairly easy to do: most objects don’t move very far in one-30th of a second. Nor does the algorithm need to know what the object is; it just has to recognize, say, corners and edges, and how their appearance typically changes under different perspectives.

The MIT researchers’ new system essentially treats unrelated images as if they were consecutive frames in a video sequence. When the modified motion estimation algorithm tries to determine which objects have “moved” between one image and the next, it usually picks out objects of the same type: it will guess, for instance, that the 2006 Infiniti in image two is the same object as the 1965 Chevy in image one.

SeeIT.com

 SeeIT.com is in beta while the company is scaling the index from millions to hundreds of millions of images. You can try it by clicking here, then entering the user name picture and password picture93AE (exclusive access for Search Engine Land readers). See thisinformation for new users for more information, including some of the limitations of the current beta release.

Riya

Riya started out focusing primarily on facial recognition, but now has a beta visual search that lets you find similar faces and objects on many images across the web and then refine your results, using color, shape and texture.

Riya also powers the visually oriented product search service Like.com that lets you find clothing and a few home furnishing items based on visual similarity. Like also has a “celebrity” search that lets you see what the stars are currently wearing and find similar accoutrements for your own adornment.

DARPA building search engine for video surveillance footage

According to a prospectus written in March but released only this month, the Video and Image Retrieval and Analysis Tool (VIRAT) will enable intel analysts to “rapidly find video content of interest from archives and provide alerts to the analyst of events of interest during live operations,” taking both conventional video and footage from infrared scanners as input. The VIRAT project is an effort to cope with a growing data glut that has taxed intelligence resources because of the need to have trained human personnel perform time- and labor-intensive review of recorded video.

Check out the “simple’ diagram accompanying the article. This stuff isn’t simple.

VideoSurf: New, Genuinely Radical Video Search

VideoSurf is a computer vision search engine that processes all of the kinds of information most video search services do, but then goes a step further, applying a proprietary process using “multigrid fast computation” and some heavy-duty computer processing power to analyze videos, identify people, and extract all kinds of additional information directly from the video itself. Until I saw the demo, I thought this type of technology was still years away.

Apple wins patent on 3D object-recognition technology

 The USPTO has awarded Apple a patent on 3D object-recognition technology that goes well beyond the current face recognition already included in apps such as iPhoto and the iOS 5 camera application, allowing a device to “build” a 3D face or object by analyzing the curves, contours and shadows of a 2D image. Such technology would give Kinect-like detection and recognition capabilities to cameras such as those found in iOS and Mac devices.

Another benefit of the Polar Rose acquisition, where the technology behind this patent was developed.

Developer APIs

Kooaba

The Swiss company aims to unlock its library of over 10 million images, ranging from album covers to books and movie posters, and provide access to all that precious data via the cloud.

Kooaba hopes that the launch of the API will trigger third-party developers to develop more mobile applications –iPhone and Android versions exist already – or tools that tap into social networking services like Facebook and Twitter, etcetera.

OpenCV

OpenCV (Open Source Computer Vision) is a library of programming functions for real time computer vision.  It has C++, C, Python and soon Java interfaces running on Windows, Linux, Android and Mac. The library has >2500 optimized algorithms.

Out at the Leading Edge of technology

New Image-Recognition Software Could Let Computers ‘See’ Like Humans Do

Using such small amounts of data per image makes it possible to search for similar pictures through millions of images in a database, using an ordinary PC, in less than a second, Torralba says. And unlike other methods that require first breaking down an image into sections containing different objects, this method uses the entire image, making it simple to apply to large datasets without human intervention.

Developing artificial intelligence systems that can interpret images

 Torralba is also attempting to develop systems that can scan a short video clip and predict what is likely to happen next, based on what people or objects are in the scene. To do this, the systems will need to understand what actions each object or person in the scene is capable of making, and what their limitations are. This will allow the systems to make predictions about what each of these entities is likely to do in the near future.

We already have facial detection in our software, and identifying the person is definitely coming. There are technologies to recognize a smile in a cheap iPhone app, or recognize human emotion currently exploited for focus group work. There are computers patrolling prison yards and making sure doctors and nurses wash their hands between patients. There is no doubt in my mind that pre-edit processing will give editors name, context and emotion metadata. And smart companies, like us, will exploit that as input for automating certain editing tasks.

Essentially Prelude is an ingest, logging and rough cut tool. It’s designed to make it easy to add log notes (a.k.a. metadata) quickly and easily, then perform a rough first string out to send to a craft (skilled) editor for the real work. Now obviously, any tool that encourages the entry of metadata is good by me!

So far, there’s not page for Prelude I can link to but from what I remember the key features are:

• Works directly off non-tape media
• Media can be previewed and selects trimmed before ingest to Prelude
• Icons can be “skimmed” (I think Adobe has a different term) FCP X style.
• Will copy media from the card to a specified location
• Will (optionally) do proxy (or really any specified format) generation in parallel with ingest
• Has great tools for subclipping and adding log notes easily
• Can build simple cuts-only timelines
• Exports direct to Premiere Pro or to Final Cut Pro 7 XML.

That’s about what’s known right now. Given that Al finished off by saying “See you at NAB” I can only guess we’ll learn more then (or perhaps before).

Adobe's Al Mooney previews Prelude. Shown here is the simple timeline assembly tool.

I’ve long said that there is no credible support for the ridiculous figures of “loss” to the US economy either in dollars or jobs. Even the US Government Accountability Office says there is no credible support for any of the ridiculous figures of loss promulgated by the MPAA and RIAA. And yet, the numbers are repeated by politicians and the mainstream media to “prove” that “piracy” is a problem “we can all agree on”.

No we do not “all agree”. Until you present evidence that there really is harm by using independent, credible and peer-reviewed studies, we simply do not believe your BS.

This article from the Cato Institute attempts to find the “facts” behind the ridiculous figures and finds exactly what I found: nothing. Zip, nada.  According the the real results of even studies by funded by the SOPA supporters, amount to nothing more than the global gross of Alvin and the Chipmunks!

Julian Sanchez takes down all the stats in a most thorough way.

But it misses the point and Nat Torkington nailed it brilliantly. I’d post the whole thing but that would not be right. It’s short, go read it.

He brilliantly juxtaposes a story about a farmer who’s property is flooding and house being inundated and yet he rejects help from a neighbor in a truck before the waters rise, a boat when it’s above floor level, and a helicopter when he’s on the roof. In each case he rejects the help claiming he’s praying and “relying on God”.

Well, Bob drowns. He goes to Heaven and finally gets to meet God. “God, what was that about? I prayed and put my faith in you, and I drowned!”

God says, “I sent you a truck, a boat, and a helicopter! What the hell more did you want from me?”

And that is *exactly* the point. In speaking to the proponents of SOPA/PROTECT IP (MPAA/RIAA) he nails it so completely it should be published far and wide:

All I can think is: we gave you the Internet. We gave you the Web. We gave you MP3 and MP4. We gave you e-commerce, micropayments, PayPal, Netflix, iTunes, Amazon, the iPad, the iPhone, the laptop, 3G, wifi–hell, you can even get online while you’re on an AIRPLANE. What the hell more do you want from us?

Take the truck, the boat, the helicopter, that we’ve sent you. Don’t wait for the time machine, because we’re never going to invent something that returns you to 1965 when copying was hard and you could treat the customer’s convenience with contempt.

Don’t wait for the time machine. Business models *always* change and if you don’t adapt, you die. Sometimes you can use your influence and money to buy a few more years by buying a few votes in Congress while distributing completely inaccurate information (less politely called lies).

There’s not going to be a time machine. Any legislative bailout you get will only delay the inevitable: business models for entertainment funding and distribution have already changed, it’s just some people haven’t realized it yet.

And Nat, sorry I still managed to quote way more than I should of your article, but since I couldn’t say it better, what was I to do?

Now, before I start let me be clear. I am NOT saying timecode is unimportant. I’m NOT saying that timecode is passé and suddenly irrelevant. Timecode remains incredibly important for any tape based access.

What I am saying is that text search – or phonetic search derived from text – is becoming a highly viable, and in many ways superior, way to search and find content. Timecode’s primary role was in being able to identify any given frame from a tape by tape and frame number. There’s nothing wrong with that approach, but as humans we don’t think in “reel and Timecode”, which is why text is a superior option.

In this technology summary, I’m going to consider:

• What tools are at our finger tips right now and their relative merits,
• Why speech transcription is ultimately more valuable than phonetic search (long term),
• Developments in speech transcription, and
• Transcription technologies.

Software and tools available now

There are two broad approaches to text search in the marketplace today: those that transcribe the speech into text and those that use phonetic search. In the first category we have Adobe Premiere/Audition/Soundbooth using Autonomy’s technology to transcribe speech to text. In the second we have Avid and Boris using Nexidia’s phonetic search technology in Media Composer (for Phrasefind and Script Sync) and Soundbite (formerly Get!) respectively. Our own prEdit edits video using a text transcript.

Phonetic search makes no attempt to understand meaning. Essentially, Nexidia’s technology “understands” what an audio waveform would look like when the text is input. Nexidia scans all the audio files ahead of search and indexes them for search. When you input a word, it estimates the waveform then finds matches for that waveform by comparing the index with the target waveform. This technique is great when you don’t have a transcript, or have a transcript without any time stamps.

This technology is also used to align a text file (script) with the audio in the audio and video files in Avid’s ScriptSync with the optional ScriptSync package. This also works great if you have a transcript of interviews to align the audio in the video with the transcript. However, if you have a transcript available, working with prEdit may be faster, manipulating the audio and video by editing and modifying text.

Going the other way – from speech to text – provides an additional advantage in that we have meaning associated with the text, and we have a transcript that carries through production into distribution. If you want searchable text for distribution then Adobe’s professional tools are the only automated tools: phonetic search isn’t viable because it would require distributing the Nexidia engine into distribution (and with their licensing model, I don’t see that happening any time soon).

However, the speech to text engine used by Adobe (licensed from Autonomy) is still a work in progress: results can be quite good but the average result is less spectacular, sometimes completely useless. That is why Adobe have added, and strongly pushed, the ability to provide the speech transcript engine a guide script. Surprisingly, a guide script alone – even one where it is an exact transcript – does not transcribe perfectly. The best results require a trip via Adobe Story. This has the advantage of keeping all punctuation and paragraph breaks  (and automatic subclipping into paragraph subclips in prEdit). For a comparison on accuracy you might be interested in Colin Brougham’s comparisons.

The big disadvantage to this approach is that it requires a transcript, the very thing most people want to automate because of the cost.

Why speech transcription is more valuable than phonetic search

Speech transcription carries meaning. Phonetic search does not carry meaning. This is an important distinction, because it means that speech transcription is valuable metadata as well as a production tool, while phonetic search is a useful production tool, but has no value as metadata away from the Nexidia engine.

Speech transcription can be carried with the media throughout its life, even into edited versions. Indeed, this is Adobe’s intent. They tend to focus on the speech transcript metadata as part of a distribution strategy more than its use in postproduction. The speech transcript metadata is carried inside media files as XMP metadata. (There are other alternatives for speech transcript metadata in distribution files, but I’ll discuss that further down.)

Speech transcription can be searched by anyone, at any stage, without needing a proprietary engine.

Speech transcription can be used to derive keywords and other expressions of meaning, which is valuable not only for automating some types of production (some types folks, only some) but extremely valuable as metadata for later finding content.

Transcribed speech is the input to prEdit. Briefly prEdit allows you to easily add metadata (log notes), break interviews into thought segments and eliminate less useful material, before searching and building a story by dragging and dropping text blocks. Editing can continue through the story building process and narration added (converted to voice instantly). At any time you can preview a clip or clips, the full story, or any selected part of the story before exporting to Final Cut Pro 7 or Premiere Pro CS 5.5 or later.

So, while phonetic search is a great post-production tool, transcription into real text has a wider range of uses and uses outside post-production. The only trouble is, speech transcription is expensive!

Developments in speech transcription

It’s a great time to live if you’re interested in speech transcription. The most significant developments have been hybrid computer-human approaches used by 3PlayMedia, SpeakerText and SpeechPad to reduce the cost and time of transcription. These companies particularly are focused on the need for transcription for video in distribution, but are excellent choices for transcriptions for prEdit or other postproduction needs.

Until we get a fully automatic speech transcription with adequate accuracy, these will help. Even with human correction some uncommon words or names will not be transcribe accurately.

Transcription technologies

I’ve talked about Nexidia and the phonetic search technology and why my long term preference is for speech transcription, so it’s no surprise that I spend some time following what’s happening with the technology. What is interesting to me is that the two companies who are generally recognized as having the most accurate under the widest range of conditions are not (yet) available for postproduction work.

Google has been amassing huge numbers of examples of speech for recognition – an important first step to accurate speech recognition – via the (now defunct) Goog411 initiative. Google have been using this technology for automated captioning for YouTube videos and for voicemail transcriptions within Google Voice. As a Google voice customer I’d say that the results are definitely much better than the attempts by Vonage (laughable) and comparable or better than Premiere Pro’s use of Autonomy.

More information on Google’s voice recognition plans can be found in this Techcruch article with Mike Cohen, head of Google’s Voice recognition efforts. Also interesting to note is that Google is slowly opening up an API for their speech recognition efforts, starting with Chrome version 11. How open that is for third party developers to use, remains unknown, but it’s an interesting direction from the search giant. If the API became open, and this is one of the two most accurate speech transcription technologies, why wouldn’t savvy developers like us, start to use it and integrate it into our software?

Equally prominent in the speech transcription/recognition community is Nuance, probably best known for powering Dragon Dictate, Dragon Naturally Speaking (and the variations) and the speech recognition component of Apple’s Siri technology. (Siri adds a lot of powerful tools on top of this basic recognition layer, but if the speech isn’t recognized accurately nothing good can come from it downstream.) There is no public API for any of Nuance’s technology (nor Siri for that matter). Nuance tends to do direct deals with companies who want to license its technology – a fairly standard practice in the technology world.

My fondest hope is that Apple’s license from Nuance will be extended to OS X and a speech recognition framework be included in OS X for developers. It’s a fond hope, not anything real!

Google and Nuance have the most accurate technologies that do not require speech training. I’m a Dictate user but that product uses training to obtain high (very high) accuracy in transcription. To be able to be accurate without needing training is what we need for interview transcriptions for post.

Tip: If you do have Dictate or any of the PC variants of Nuance’s products, one technique is to listen to the interview and speak it with your own (trained) voice. I’m not yet there, but it is possible to speak-as-you-hear (the basis of the common ear-bud presenter trick) for fast, accurate transcription.

Beyond the giants, there are many other technology companies, or open source projects, in the speech recognition field that are worth mentioning. One thing that should be noted is that most of these technologies are cloud based (as is Apple’s Siri). They work as long as they have a connection to their primary servers.

I’ll let these folk speak for themselves. Whichever technologies prevail, we’re definitely seeing a surge in the accuracy and flexibility of speech recognition, that is going to factor in post production in the coming years, beyond where we are at right now.

SeedVocal SDK

Speech Recognition for your iPhone application. The claim is that the technology is:

• currently unavailable from Apple APIs
• easy to add to your application
• convenient for all types of apps: games, fun and promo applications or utilities
• suitable for the iPhone and the iPod Touch
• cheaper than you might expect.

Sphinx4

Sphinx-4 is a state-of-the-art speech recognition system written entirely in the Java^TMprogramming language. It was created via a joint collaboration between the Sphinx group at Carnegie Mellon University, Sun Microsystems Laboratories, Mitsubishi Electric Research Labs (MERL), and Hewlett Packard (HP), with contributions from the University of California at Santa Cruz (UCSC) and the Massachusetts Institute of Technology (MIT).

Sphinx-4 started out as a port of Sphinx-3 to the Java programming language, but evolved into a recognizer designed to be much more flexible than Sphinx-3, thus becoming an excellent platform for speech research.

Speech Recognition with Javascript; speechapi.com

With speechapi.com’s javascript API, it is possible to build interesting speech-web mashups that include both speech-to-text as well as text-to-speech.

A combination of several technologies and open source tools make this possible. In the browser, Flash is used to access the microphone and stream the audio to an RTMP server. Red5 is used because its a versatile media server that has the benefit of being open source and free.

Open-Source Large Vocabulary CSR Engine Julius

“Julius” is a high-performance, two-pass large vocabulary continuous speech recognition (LVCSR) decoder software for speech-related researchers and developers. Based on word N-gram and context-dependent HMM, it can perform almost real-time decoding on most current PCs in 60k word dictation task. Major search techniques are fully incorporated such as tree lexicon, N-gram factoring, cross-word context dependency handling, enveloped beam search, Gaussian pruning, Gaussian selection, etc. Besides search efficiency, it is also modularized carefully to be independent from model structures, and various HMM types are supported such as shared-state triphones and tied-mixture models, with any number of mixtures, states, or phones. Standard formats are adopted to cope with other free modeling toolkit such as HTK, CMU-Cam SLM toolkit, etc.

I have no idea what that means either!

Scripto

Scripto is a light-weight, open source, tool that will allow users to contribute transcriptions to online documentary projects.  The tool will include a versioning history and full set of editorial controls, so that project staff and manage public contributions.  The design and development of the tool is being supported by grant funding from the National Endowment for the Humanities, Office Digital Humanities, and the National Historical Publication and Records Commission.

Encoding All

From an audio or video media, transcription can automatically generate a file ofsubtitles, the keyword list in XML format and the entire plain text.  (That’s the translation from the French – speech recognition is not limited to English!)

And interesting, although not currently speech recognition:

Soundhound

Currently, SoundHound’s specialty is delivering information about music. Users can sing or hum a tune into its SoundHound app and the app returns the song name, as well as other information. Last week, the company released its Hound app, which can identify when a user says the name of an artist or album.

The slightly frivolous-seeming “name that tune” aspects of SoundHound’s applications belie the seriousness of the technology and business underneath it all. SoundHound has raised $16 million in venture capital and currently has 55 full-time employees. Investors have been attracted to the company by the future potential of SoundHound’s core technology, Mohajer told me. “We own all of our technology, while a lot of other apps in this space license their core technology,” he said. “We built everything in-house and we own all of our intellectual property.

My first thought when reading that headline was “well, d’oh”, because it seemed like an oversimplification of the scale of the problem. Sure, if everyone switched over to streaming video for their media consumption, then we’d be in a different position. Trouble is, people generally are watching more television than ever, via more traditional channels than internet delivered streaming content.

Ultimately typical viewers like the type of content they find on traditional television. The secret to success in the new world is more of that type of content. Netflix and Google’s YouTube are all investing in new content because the traditional suppliers (i.e. studios) want ever higher license fees for their existing content.

Instead of tossing a bunch of cash into accomplished shows, Slash Gear says Netflix is heading into theoriginal programming market and YouTube is launching over 100 exclusive original channels, as reported by the Associated Press. However, getting those shows to the level of “The Big Bang Theory,” “True Blood,” or even “Dexter” will be the challenge facing streaming companies. For streaming companies, the money makes more sense if they own the rights to the shows and even create them in the first place.

The issue is pretty clear. Penetrating an existing market, like television, requires a great product, and while Netflix, YouTube, and even Apple have the cash to create a quality television show, that is not exactly the business the companies are known for. Challenging the content landscape is going to be an uphill battle, but it could be the move that changes television forever.

As I hypothesized back in December 2009, if the studios don’t make nice with new players, they have the means to pay producers - those that make this high value content now – directly, bypassing the studio and the networks.

This just in from Variety: Hulu orders first scripted original skein (that’s Variety-speak for “yarn” a.k.a series).

Last week, Hulu disclosed 2011 results that showed growth on both fronts. Hulu revenues increased 60% year over year to $420 million while its sub base reached 1.5 million.

In a blog post announcing his company’s results, CEO Jason Kilar pledged to spend $500 million on content in 2012.

Increasingly it looks like the future for Netflix, Hulu and YouTube is original programming. Do you really think Apple will have another path?

According to the Department of Labor statistics, employment in the entertainment industry has increased:

Why yes, that does show that the industry grew nicely from 1998 to 2008… all the while we were being told it was being decimated by piracy and no one could find work any more. Oh, and check out that last line. Independent artists, writers and performers jumped from 35.2k in 1998 to 50.4k in 2008 — the first decade of real mainstream internet infringement (Napster arrived in ’99). If you’re not quick with the percentages, that’s a pretty astounding 43.2% growth rate. And, it appears the BLS continues to think that jobs in that sector are going to grow over the next decade as well. Damn those pesky facts.

But surely all that “piracy” – that drives the MPAA/RIAA to lobby for truly bad laws like SOPA/PROTECT IP – has done some harm? None that’s apparently provable. This article - How Much Do Music and Movie Piracy Really Hurt the U.S. Economy – quotes the figures of “harm” that are propounded by the usual suspects and finds:

The good news is that the numbers are wrong — as this post by the Cato Institute’s Julian Sanchez explains. In 2010, the Government Accountability Office released a report noting that these figures “cannot be substantiated or traced back to an underlying data source or methodology,” which is polite government-speak for “these figures were made up out of thin air.”

More recently, a smaller estimate — $58 billion – was produced by the Institute for Policy Innovation (IPI). But that IPI estimate, as both Sanchez and tech journalist Tim Lee have pointed out, is replete with methodological problems, including double- and triple-counting, that swell the estimate of piracy losses considerably.

These figures were made up out of thin air. That’s not from a pro-piracy lobby group, that’s the US Government Accountability Office, who categorically state that there is no basis for any of the scare mongering figures of “damage” done. No evidence. In fact there has NEVER been any peer reviewed studies that show ANY harm from unauthorized distribution.

And yet the idiots at the MPAA and RIAA – and their bought-and-paid-for stooges in Congress – insist on lying to the Government, their artists and the public. Ironic, since they’ve been so totally wrong about technology in the past.

In this episode Terence and Philip discuss the outsourcing of editing jobs, remote (a.k.a cloud) editing and automation in production. What’s happening now and how will it evolve in the near future. Philip also sneaks in a preview of an interesting show he’ll be involved with in 2012.

I have a friend who used to do a large amount of bonus material for one of the major studios as they progressed through releasing their back catalog. He has already noted that, as of about two years ago, that work dried up completely and now is no longer part of his business, which fortunately is still fairly healthy for him.

This is a depressing reality check, and is probably the counter argument to “the year of Transmedia“. Transmedia is seriously expensive to produce!

The same thing that happened to music is going to be true of books. The typical ebook costs about $10 in out of pocket expenses to write (more if you count coffee and not just pencils). But if we add in $50,000 for app coding, $10,000 for a director and another $500,000 for the sort of bespoke work that was featured in Al Gore’s recent ‘book’, you can see the problem. The publisher will never have a chance to make this money back.

Sure, there will be experiments at the cutting edge, but no, they’re not going to pay off regularly enough for it to become an industry. The quality is going to remain in the writing and in the bravery of ideas, not in teams of people making expensive digital books.

The market didn’t really make a conscious choice here, but the choice has been made: it’s not a few publishers putting out a few books for the masses. No, the market for the foreseeable future is a million publishers publishing to 100 million readers. Do the math. Lots of choice, not a lot of whistles. And no bells.

Now, I’d want to take issue with the assumption that factory work or farming is that common as a career/employment these days – certainly not farming – but I think the point was rather that “Someday, writing code could require as few skills as farming or factory work”.

Currently writing code is a combination of highly technical skills, attention to detail and a need for creativity in solving the problem and writing the code. Although I am a software product manager, I do not write code because it seems incredibly complex.

Just like editing!  Both require a combination of creative and technical skills now.

Roy Bahat, president of gaming media company IGN Entertainment, writes that coding is:

Like farming was in the 17th century, factory work during the industrial revolution, construction during the Great Depression, and manufacturing after World War II….Code may one day be a basic workplace expectation – like emailing, or “proficient in Word.” Young people are also willing to learn: coding now has a brand. The kid who writes an iPhone or Android app, these days, gets the girl (or boy!).

That reminds me of my long held opinion that video production is rapidly becoming “another form of literacy“. If coding can, why not video?

About 18 months back I started a blog post “Apps are the future of distribution” but beyond the title, didn’t really have my thinking clear enough to finish the post. Fast forward and now Erick Schonfeld expresses what I could not.

While I’m excerpting a few paragraphs I strongly recommend reading the whole article to get a full feel for it.

Except there already is a global market for digital media. They are called apps, and they represent the future of media in many ways. Apps are media. Not only are they a form of media in the way that consumer software and games have always been considered media (they compete with TV, books, and music for consumers’ time and attention). But increasingly, they are also subsuming other forms of media.

I think this is probably the crucial point he’s making:

 The lines between software and media will become harder to tell apart as apps begin to include more and more traditional media.

And I can’t put this any better:

A song or a TV show will become a hit because it is shared by millions of people on Facebook and Twitter, not because it is getting millions of dollars of promotion on radio or TV. These apps will determine what we watch next through social and algorithmic recommendations—because how else do you find something to watch when traditional programming is dead?

The apps that deliver this media will exert a powerful influence over our consumption habits—what we watch, listen to, and read, as well as how we do it. Apps will help us find media through social and other filters, and throw it onto our TVs, iPads, stereos or whatever device is handy. They will bypass the set-top box, the radio, and the book store. Apps are where media consumption will happen. Media companies can continue to ignore or fight that trend at their own peril.

From the article:

All of my stuff is pirated. Everything from my HDR Video Tutorial to eBooks to Apps. Fine. It’s all there on PirateBay and MegaUpload and all that stuff. Here are the reasons why I don’t mind:

His five points:

1. It’s a cost of doing business on the internet (and indeed it is).
2. People who don’t buy now, may buy later. (In my case, I don’t mind that the next generation grows up “knowing” I know my stuff.)
3. “Pirates” are people too and not just in it for themselves. They may not be able to afford it right now.
4. Pirates have friends with money and the piracy serves as publicity. (Well, d’oh but the RIAA/MPAA are totally unable to see that despite the RIAA members regular payola scandals where they pay radio stations to “pirate” their material, it’s just authorized at that point but functionally no different)
5. As soon as he opened everything up under a Creative Commons license his business grew!

This totally parallels my experience. I know for sure that there are copies of the HD Survival Handbook and probably all my books published as unprotected PDFs, but I’ve made a very good return for the work I put into that book, and don’t feel the need to “stamp out piracy” even where I know it exists.

Of course all predictions are subject to change but Steven Rosenbaum takes a shot at it in this Fast Company article. Naturally Fast Company are going to be more focused on the business side, rather than production side, so no “large sensor” type predictions here instead the five predictions are below with my comments.

Prediction #1. 2012 is the year all video goes a la carte.

I seriously doubt that all content will go a la carte in 2012. Hopefully some moves in that direction but to have even all cable channels available a la carte in 2012 is highly unlikely because it’s just too big a shift in thinking to happen that quickly. That it is a trend that will grow somewhat in 2012, is likely.

Prediction #2. 2012 will be the year of the OverTheTop revolution.

“Over the top” = “cord cutters” = “Internet set top boxes” like Boxee, Roku and Apple TV (among others). Definitely a trend toward dropping the cable subscription in favor of broadcast HD (free with an antenna) and a combination of Hulu and other sources for content, but will this year be the tipping point? Very highly unlikely. Cable subscriptions seem to be dropping but nowhere near fast enough for any cable company to fee the pressure in 2012.

Prediction #3. YouTube and Google TV will merge (really this time). 

Perhaps, but we won’t really care as all the action is around YouTube where money is going into original production.

Prediction #4.  Yahoo will emerge as a big creator and distributor of video.

Again, perhaps, but within the span of the next 12 months we won’t necessarily see the results. Yahoo went down the path of content creation a few years back and lost a boatload of money on it. As a company that doesn’t have strong reserves (or necessarily a strong future at all) to pull off a major turnaround and be a player in creating and distributing video with 12 months, it’s highly unlikely. Maybe the deal to distribute ABC News will come to something but it’s hard to get excited over that single deal indicating anything significant.

Prediction #5.  Business video will arrive as a real targetable business opportunity.

Yep, got to agree with this one. The use of video in business promotion – both online and in real life – is growing dramatically. Branded entertainment – where the brand pays to produce a program(s) directly – is in major growth and shaping up to be one of the viable methods of funding production in the future. (There will be an upcoming blog post summarizing the sorts of deals done in recent years.)

Rosenbaum finishes with:

The next twelve months are going to be transformative. Web video will become simply “video”–made everywhere and consumed everywhere. And brands and companies, who’ve contemplated using video to tell their stories or connect with consumers, will find that the train is leaving the station. It’s time to get on board the video express, or be left with an unpunched ticket in your hand.

That I also agree with.

He starts by acknowledging what people thing:

The current and perhaps scariest Bogey Man yet is the INTERWEBS (duhn-duhn-DUHN!). The most recent doom-sayers predict ominously that audiences will “cut the cord” in favor of online video on their iPads, laptops and even on their big screens. The latest example of this gloom-o-logy is on full display in “The Death of Television May Be Just 5 Years Away” by Jim Edwards.

He then presents the evidence that television consumption has gone up by 25% recently because (or despite) many more outlets.

Technology not only did NOT erode viewing, it did the seemingly impossible, it actually created time — a new hour every day, seven new hours every week, 365 new hours — more than 15 additional days — every year. More people watch more television, now, than ever.

Once again, television has just refused to die. It has evolved.

As for those cord cutters… it is true that over the past year, paid TV subscriptions have flattened, or even declined. And, yes, technology has given audiences the power to disintermediate the advertisers who sponsor much of what is produced. These are indeed worrisome trends for an industry that relies on cable subscriptions for an enormous part of its revenues and advertising for most of the rest. It means that those of us in the business must adapt to the changing needs of the audiences we serve, in order to better reflect the value they attach to the programming we provide. We must change or risk becoming a sequel to the music industry.

I’m not entirely convinced the Networks can evolve fast enough, but they are not the essence of Television – the relationship between the producer and the audience is what’s paramount, and there are enough alternate channels being developed (and could be developed) that would make the Networks and Cable Channels as they are currently structured, irrelevant.

Clay Shirky has an interesting perspective in the Collapse of Complex Business Models, that suggests the current power players may end up out of the picture if they can’t adapt – and Shirky’s point is that complex systems can’t adapt, only become more complex until they collapse.