Categories
Media Theory

Why is the Demand for Quality Video so High?

Sam Mestman wrote a post at FCP.co that I haven’t been able to get out of my head. Ostensibly it’s career advice for people just getting out of film school, but without meaning to do so Mestman touches on a profound question:

Small businesses have no idea how to market themselves through video, they all have small budgets for marketing that they waste on hideous content that doesn’t work, and there’s a big market in just about every town for someone who makes great, affordable web and social media video for businesses. [Emphasis mine]

If the demand for “great, affordable web and social media video” is so high, then why is that demand going unmet? Could it be that the difficulty of creating great video is orders of magnitude more than what’s affordable for small businesses.

Let’s consider three different methods of nonfiction storytelling: writing, podcasting, and documentary. When you consider the amount of time, effort, and skill required to make a great article, versus a great podcast, versus a great documentary; the difference is probably logarithmic.

Think about the standard sit down interview common to all forms of nonfiction storytelling. When a reporter talks to his subject, from the moment the interview starts until it ends, the reporter is able to use whatever they’ve observed.

When a podcaster conducts an interview they have to consider the overall sound quality and the temporal nature of audio recording itself. If the subject says something brilliant, but the recording wasn’t running in that moment, then it might as well not have happened. Ditto if the sound quality is poor. The podcaster has multiple dimensions of difficulty that the reporter can blissfully ignore.

The documentarian has all of the reporter’s and podcaster’s problems, in addition to all of the problems that come with adding image (camera equipment, lighting, composing, etc). Because there are so many considerations, the documentary often requires a crew of specialists, adding personnel management and financial components to the challenges.

We can consider each medium’s difficulties with the following table:

ReporterPodcasterDocumentarian
Getting the Interview– the Interview
– Sound Quality
– Temporal Recording
– Interview
– Sound Quality
– Temporal Recording
– Image Quality
– Image Composition
– Crew Coordination

To be clear: I’m not saying that someone like the documentarian Alex Gibney is more skilled than the writer Susan Orlean. What I am saying is that creating a documentary for HBO is cumulatively probably more difficult than writing an article for the New Yorker.

This isn’t meant to be scientific, but over the next few posts, let’s keep this idea in mind as we explore what makes video production unique.

Categories
Media Theory

The Soul of Nonlinear Editing

I keep thinking about Tom Ohanian’s series on the State of Digital Nonlinear Editing. Specifically these paragraphs in Part 10:

Content that is recorded will then be processed by a variety of AI application suites. Each suite will provide different functionality (e.g. tonal analysis, speech-to-text, etc.) based on the characteristics of the content. … Very rich, detailed, and comprehensive metadata about that content will result without the large number of humans currently associated with these tasks.

At that point, the user will be presented with the text associated with the content. Each word, with exact reference to its precise positioning within the data stream, will be indexed. Manipulation of text (e.g. cut, copy, paste), will, in effect, be the method of editing that content. Picture and sound will follow along. [Emphasis mine]

Readers of my blog know that I think machine learning is going to revolutionize the edit suite; mainly by reducing the need for Assistant Editors to perform ‘mechanical’ tasks like Ingesting, Sync-ing, and Grouping. But I don’t agree with Ohanian here. And I think his point of view, that editing is basically mechanical, represents one of the problems we face when trying to discuss the future of nonlinear editing.

Editing is a visceral experience. Full stop.

Editing will never be as easy as cutting and pasting text because what’s being said is often secondary to how something’s said. Think about the Brett Kavanaugh hearings. You could read transcripts all day long, but his anger is what left its lasting impact.

The primacy of subtext is applicable to all genres of editing, from the biggest tentpole blockbuster to most corporate HR training video. Anyone who’s listened to multiple reads of Voice Over will know firsthand that the same words spoken differently feel very different each and every time. What makes every editor unique is how these subtle differences inform their creative process.

The source/record metaphor is probably a dated way to interact with audio/video media; and smarter tools that assist the editor in finding and selecting media are needed. But I think “Marking IN and Marking OUT to create edit points” is going to be with us for a while because Marking IN and Marking OUT is editing. The problem isn’t the model, it’s that we need to expand our definition of literacy to include video.

Categories
Media Theory

Recorder. A perfect Machine Learning use case.

Atlas Obscura and the New Yorker report on a new documentary about a remarkable woman, Marion Stokes, who recorded 70,000 (!!) hours of television on VHS tapes from 1975 until 2012.

Marion Stokes was secretly recording television twenty-four hours a day for thirty years. It started in 1979 with the Iranian Hostage Crisis at the dawn of the twenty-four hour news cycle. It ended on December 14, 2012 while the Sandy Hook massacre played on television as Marion passed away. In between, Marion recorded on 70,000 VHS tapes, capturing revolutions, lies, wars, triumphs, catastrophes, bloopers, talk shows, and commercials that tell us who we were, and show how television shaped the world of today. 

From the documentary’s website “RECORDER: The Marion Stokes Project”.

The 70,000 VHS tapes are currently awaiting digitization by the Internet Archive to be made available to the public. But these tapes also represent the ideal use case for Machine Learning technology like Google Vision to make it all searchable.

This also clearly demonstrates the need for a new editing metaphor, something like Tom Ohanian wrote about on his excellent State of Digital Nonlinear Editing series on LinkedIn.

Because a massive amount of people can read. And if they interact with content not first and foremost via video and audio, but with words, manipulation of content becomes really easy and very accessible. And it will / should work along these lines: Content that is recorded will then be processed by a variety of AI application suites. Each suite will provide different functionality (e.g. tonal analysis, speech-to-text, etc.) based on the characteristics of the content. When a live or recorded stream of content is digitized, it will be subjected to a variety of these suites.


At that point, the user will be presented with the text associated with the content. Each word, with exact reference to its precise positioning within the data stream, will be indexed. Manipulation of text (e.g. cut, copy, paste), will, in effect, be the method of editing that content. Picture and sound will follow along.

Tom Ohanian’s State of Digital Nonlinear Editing and Digital Media 10

(Note: Linkedin’s poor formatting makes these articles more difficult to read than necessary, but stick with it, his series is very insightful and thought provoking.)

Categories
Media Theory

look at the data — but not too much

Great interview with AMC’s Josh Sapan at Recode about the benefits and limitations of using data in the creative process:

https://megaphone.link/VMP9318038387

This mirrors what I’ve been saying about using data to inform the creative process, not decide. Too bad we don’t have the tools we need yet.

Further reading:

Categories
Media Theory

Vindication :-)

In reference to: Reality and Streaming Television