The resolution-efficiency of a projection is defined as the lowest resolution of any point.
Resolution-efficiency can take values in [0,1]. A value of exactly 0 is reached by many common projections, namely those which require infinite area (stereographic, Mercator, gnomonic) and those which have a semiminor axis of zero somewhere (cylindrical and azimuthal equal-area, orthographic, Mollweide and all other pseudocylindrical equal-area projections that don't have an angle at the poles). A value of exactly 1 cannot be reached by a Euclidean projection (since it represents no distortion), but it is possible to come arbitrarily close by having lots of interruptions, or by projecting only a small region rather than the whole sphere.
Intuitively, having a high resolution-efficiency guarantees that the projection will not destroy important details, even though it may exaggerate unimportant ones. Thus, it's a measure of how much detail you can reliably show on a map of a given size, or of how small you can squeeze a map (to avoid wasting space, either physical space on a page or digital space used to store an image file) while still retaining a certain level of detail. One example where this property is useful is for storing "raw input data" that might subsequently be converted into a different projection (for example, consider how the cylindrical equal-area projection squashes the poles, and this lost detail cannot be recovered if you then want to reproject as a polar-aspect azimuthal projection). However, it's also useful for end-user display maps, where it ensures that any features the viewer might be interested in remain recognizable, even if they are distorted.
When applied to equal-area projections, maximizing resolution-efficiency is equivalent to minimaxing angle distortion. If done on the meridian-interrupted sphere, this gives the circular Hammer projection. This does seem to be unfortunate as it doesn't intuitively look like a very good projection... but no-one has yet devised a better optimality criterion for equal-area projections, and this one should at least work better for regions that are closer to circular, such as regular polygons. (After all, the azimuthal equal-area projection is optimal for circles.)
When applied to conformal projections, maximizing resolution-efficiency is equivalent to minimizing mean normalized area distortion (in fact, it's pretty much a generalization of the idea that isn't restricted to conformal projections). If done on the meridian-interrupted sphere, this gives the Lagrange projection (discussed here, here and here). This is different from minimaxing area distortion (as the Eisenlohr projection does).
However, it is also possible to optimize purely for resolution-efficiency on its own, without restricting to equal-area or conformal projections. Indeed, my original purpose in devising this criterion was to come up with a good optimality criterion that can be applied to compromise projections, and more particularly to explain what's so good about equidistant projections, without making reference to which specific lines they are equidistant along (since they are in fact not equidistant between most points, greatly limiting their usefulness for actual distance-measuring).
The plate carree projection (specifically, and not cylindrical equidistant projections with standard parallel away from the equator) obviously has optimal resolution-efficiency among cylindrical projections, and I suspect that it is optimal among all full-world projections with a convex shape, but I haven't proven this (there can, at least, exist other projections which are tied with it). The azimuthal equidistant projection is optimal among azimuthal projections, but is not as good as the plate carree projection (except when interrupted into two-hemisphere form). In any case, for the applications where resolution-efficiency is important, having a rectangular shape is often also useful in its own right.
An interesting property of this metric is that equal-area and conformal projections that are in some sense "counterparts" to each other (such as azimuthal equal-area and stereographic hemispheres, or the circular Hammer and Lagrange projections mentioned above) often have the same resolution-efficiency, suggesting that these are somehow "equally far apart" from the ideal compromise projection in the middle. The orthographic and gnomonic projections (when restricted to a circle smaller than a hemisphere, so that they have nonzero resolution-efficiency) are also found to be "counterparts" in this sense, which feels appropriate.
A related concept is that of constant-resolution projections. A projection is defined as constant-resolution if its Tissot indicatrices have the same semiminor axis everywhere. The well-known resolution-efficiency-optimizing projections (plate carree, azimuthal equidistant) are also constant-resolution, and vice versa, but there is no obvious guarantee that this will always be the case.
Some examples of resolution-efficiency of common projections:
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Full-world projections:
Plate carree: sqrt(2/pi) ~= 0.7979 (general-case cylindrical equidistant: sqrt(2/pi*cos(standard))
Azimuthal equidistant: 2/pi ~= 0.6366
Lagrange: 1/2 = 0.5
Circular Hammer: 1/2 = 0.5
Classic Hammer: 1/2/sqrt(2) ~= 0.3536
Sinusoidal: (sqrt(pi^2+4)-pi)/2 ~= 0.2913
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2-hemisphere azimuthal:
Equidistant: sqrt(8)/pi ~= 0.9003
Equal-area: sqrt(1/2) ~= 0.7071
Stereographic: sqrt(1/2) ~= 0.7071
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Azimuthal arbitrary-radius:
Equidistant: sin(r/2) / (r/2)
Equal-area: cos(r/2)
Stereographic: cos(r/2)
Orthographic: sin(r/2) / (tan(r)/2)
Gnomonic: sin(r/2) / (tan(r)/2)
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Optimal polyhedral, theoretical:
Dihedron: sqrt(8/pi/n / tan(pi/n))
Triangular dihedron: sqrt(8/3/pi/sqrt(3)) ~= 0.7001
Square dihedron: sqrt(2/pi) ~= 0.7979 (same as plate carree)
Pentagonal dihedron: sqrt(1.6/pi * sqrt(1 + 2/sqrt(5))) ~= 0.8372
Hexagonal dihedron: 2/sqrt(pi*sqrt(3)) ~= 0.8574
Tetrahedron: sqrt(pi/3/sqrt(3))/acos(1/sqrt(3)) ~= 0.8139
Diamond: sqrt(pi/6/sqrt(3))/acos(sqrt(2/3)) ~= 0.8933
Cube: sqrt(8/3/pi) ~= 0.9213
Icosahedron: sqrt(pi/15/sqrt(3))/acos(phi/sqrt(3)) ~= 0.9531
Dodecahedron: ~= 0.9699
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Optimal equal-area polyhedral, theoretical:
Dihedron: sqrt(pi/n/tan(pi/n)/2)
Triangular dihedron: sqrt(pi/6/sqrt(3)) ~= 0.5498
Square dihedron: sqrt(pi/8) ~= 0.6267
Pentagonal dihedron: sqrt(pi/10/sqrt(5-2*sqrt(5))) ~= 0.6576
Hexagonal dihedron: sqrt(pi/4/sqrt(3)) ~= 0.6734
Tetrahedron: sqrt(sqrt(3)/pi)*acos(1/sqrt(3)) ~= 0.7093
Diamond: sqrt(pi/8*sqrt(3)) ~= 0.8247
Cube: sqrt(6/pi)*acos(sqrt(2/3)) ~= 0.8506
Icosahedron: ~= 0.9191
Dodecahedron: ~= 0.9353