Patco wrote:

It think it would have to be a fixed focal length lens, as an HQ full frame zoom would have to be much too large for a compact camera.

… to which Thom Hogan replied:

As I've written many times, that's not true. It is tricky to get the design of a zoom compact enough, but as I wrote quite some time ago, it is not only possible, but there are at least three ways I know of to get there. The one that should be obvious to everyone right now is to use the Kodak/Leica offset microlens bit, as this allows you to use designs that are aggressive in the rear portion of the optics. But there's complexity in all three ways I know to do it

Sensor-Based Shake Reduction vs Optical (Lens-Based) Image Stabilization / Vibration Reduction – Duplo:

That I prefer the optical system for telephoto work has nothing to do with how effective it is, but that the optical systems stabilizes the viewfinder and thus the AF module as well and that is a slight advantage for telephoto work.

Does Having Small Pixels (High Pixel Density Camera Sensors) Equals Lousy High ISO Noise Performance? – A 6-part series of forum posts by Daniel Browning attempts to debunk the myth.

Daniel says:

Again, it is possible for small pixel sensors to have worse performance per pixel, but the same performance when actually displayed or used for the same purpose as a large pixel sensor. This fact may be unbelievable or at least counter-intuitive to many people who work with digital images, but I believe that is only because of the following five types of mistakes that are frequently made in image analysis:

* Unequal spatial frequencies
* Unequal sensor sizes.
* Unequal processing.
* Unequal expectations.
* Unequal technology.

On spatial frequency:

The first category, spatial frequency, is the most important and fundamental element of image analysis as it pertains to pixel size. This aspect of an image indicates the level(s) of detail under analysis: whether fine details (high frequencies) or or more coarse information (low spatial frequency). This is often ignored completely, other times poorly understood, but it always has a tremendous impact on the result of any comparison or performance analysis.

The great majority of image analysis is fundamentally based on the performance of a single pixel, so having worse performance per pixel and the same performance in the actual image, where it matters, would seem a contradiction. It isn't.

Performance scales with spatial frequency. In other words, the many important performance characteristics of a digital image are all a function of spatial frequency, including noise, dynamic range, color depth, diffraction, aberrations, and motion blur. Therefore, for any given sensor, analysis of higher spatial frequencies will never show better performance than analysis of lower spatial frequencies.

Every image sensor has a sampling rate, or Nyquist. This is the spatial frequency at which the image sensor samples information. But every resulting digital image also contains information at all other lower spatial frequencies. For example, Pixel A may have a native sampling rate of 30 lp/mm. But the resulting digital image also contains information corresponding to 20 lp/mm and 10 lp/mm, which are larger, coarser details. Pixel B may be much smaller, and may natively sample at 60 lp/mm, but the resulting image still contains all the information of Pixel A, it only has additional information.

100% crop is the most common way to compare image sensors, but it is very misleading when the sensors have different pixel sizes. The reason is that 100% means the maximum spatial frequency. But different pixel sizes sample different spatial frequencies. So 100% crop means higher spatial frequencies for small pixel sensors than it does for big pixel sensors. This results in comparisons of completely different portions of the image. A 100% crop of a small pixel image would show a single leaf, whereas a 100% crop in a large pixel image would show the entire shrub. It's a nonsensical comparison. Failing to account for that important and fundamental difference is one of the most common flaws in such comparisons.