Re: [mediacapture-main] A practical algorithm for constraint resolution needs to be described.

Aren't these problems theoretical rather than important to solve in 
implementations?

#### Case 3: Linked configuration parameters

Last I checked, the Logitech C910 on Windows supports about 54 
combinations of width/height/framerate/codec. The OSX camera driver 
seems to only like multiples of 40x30 and 128x72, in less than 25 
working increments (multiplied by framerate I suppose).

Why not leave it to implementations to optimize prematurely?

#### Case 2: Orthogonal configuration parameters

Orthogonal capabilities are just that, orthogonal. E.g. a { zoom: { 
min: 0.0, max: 1.0 } } capability would work the same regardless of 
other settings, which in practical terms means that "constraining on 
width/height/zoom" is no more interesting than constraining on 
width/height and then "setting" (constraining) zoom on the result.

Said differently: A settings dictionary of { width: 640, height: 480, 
zoom: x } where x allows any legal zoom value, is the logical 
aggregate of an infinite number of sets that lets you stick with the 
Case 1 algorithm. Since x adapts (by the algorithm effectively 
ignoring x) it has no 'actual' value and would not contribute fitness 
distance.

I realize this sounds like your min-max triplet, but why track changes
 to x at all, except to support contradictory combinations in the 
advanced algorithm?

    advanced: [ { zoom: { max: 0.2 }, width: 640 },
                { zoom: { min: 0.3 }, width: 800 } ]

And why would anyone care about such nonsensical cases?

-- 
GitHub Notif of comment by jan-ivar
See 
https://github.com/w3c/mediacapture-main/issues/118#issuecomment-74207377

Received on Friday, 13 February 2015 05:31:10 UTC