Here are good videos about it:
Shutter Speeds & Frames Rates:
Using shutter speed to achieve certain things:
Short explanation:
No video for now.
Shutter is slo-mo
None of the videos now.
More MPixels imply larger image files, thus slowing down image processing and file transfer. But the good news is that more MPixels do not increase image noise - despite a widespread belief to the contrary.
The reason for this is that when you scale down to a resolution required for displaying or printing, the resulting noise and dynamic range of the output pixels improve (assuming well behaved rescaling software). The resulting noise and dynamic range after scaling are then in theory identical to what you would have had if you had started off with a sensor with exactly the required resolution to begin with. And you may end up with a slightly sharper image as a bonus – but this is off-topic here.
Figure above shows an accurate analogy to collecting photons or the basic “particles” of light: measuring the rate of rainfall by collecting rain in cups. We might decide to measure the rainfall with a single large bowl. Or, alternatively, we could use for example 4, 16 or 64 smaller cups. In all these cases the effective area used for catching drops is assumed to stay the same.
In the example with 64 cups, I exposed these cups to a simulated rainfall that caused each cup to get on average 5 drops of rain during the exposure. For visual clarity I used really big drops or – if you prefer – minute cups. However, for the signal-to-noise ratio the size of the cups doesn’t matter. Due to the statistics, on average only 17% of the cups will contain exactly 5 drops of rain after the exposure. Some cups will instead have 4 drops (17% chance) or 6 drops (15% chance), but some may contain 9 drops (4% chance) or even remain dry (0.7% chance) during the exposure to the rain.
This phenomenon explains a major source of pixel noise. This source is unavoidable and especially noticeable with small pixels, in dark shadows or at high ISO settings. The corresponding light level is shown in Figure as a projected gray-scale image below the cups: empty or near-empty cups correspond to black pixels and full or almost full cups correspond to white pixels.
Now let's look at an array of 16 (instead of 64) cups. Each cup is 4× larger and will thus, on average, catch exactly 20 drops instead of 5 drops. But, after scaling, the measurements obviously result in the same estimated rainfall. Due to Poisson statistics, we may occasionally (9% chance) encounter exactly 20 drops in a cup, but we may also encounter 18 drop (8%), 21 drops (9%), or 25 drops (5%). The odds of observing 4 or 36 drops are very small but non-zero. So, although larger cups will have slightly more absolute variation when expressed in drops than smaller cups, the relative variation expressed in volume of water per surface will actually decrease as the cup size increases.
The point here is that proper scaling allows us to get exactly the same signal and noise levels using many small cups (pixels) or using an equivalent surface area covered with a few large cups (pixels). Thus a set of 4 cups will give you exactly the same information as a single bigger cup with a 4x larger surface area would have: just carefully pour the content of the 4 small cups into one big cup before measuring. Or weight all 4 cups together and subtract their empty weight.
See in more detail at http://www.luminous-landscape.com/essays/dxomark_sensor_for_benchmarking_cameras2.shtml
Check good article - http://www.pcmag.com/article2/0,2817,15465,00.asp
Good video about it:
Another one:
Good explanation:
Check following two part video
Some learning resources