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(* The pixel number of 6,000x4,000 ist the number of "effective pixels". The sensor usually has a few extra rows of pixels on all four sides, which explains the sensor resolution of 24.3 MPixels often stated, but no information about the exact image size available.) 6,016 4,000 24,064,000 24.1 Nikon D3300 Canon M50: 6,048 4,032 24,385,536 24.4
The Nokia 808 (2012) surpasses compact cameras with its 41 million pixels, 1/1.2" sensor. [20] Sensor sizes of 1/2.3" and smaller are common in webcams , digital camcorders and most other small devices.
An image that is 2048 pixels in width and 1536 pixels in height has a total of 2048×1536 = 3,145,728 pixels or 3.1 megapixels. One could refer to it as 2048 by 1536 or a 3.1-megapixel image. The image would be a very low quality image (72ppi) if printed at about 28.5 inches wide, but a very good quality (300ppi) image if printed at about 7 ...
For example, a 2-megapixel camera of 20-micrometre-square pixels will have worse resolution than a 1-megapixel camera with 8-micrometre pixels, all else being equal. For resolution measurement, film manufacturers typically publish a plot of Response (%) vs. Spatial Frequency (cycles per millimeter). The plot is derived experimentally.
For example, Sony Alpha SLT-A58 has 20.1 megapixels on an APS-C sensor having 6.2 MP/cm 2 since a compact camera like Sony Cyber-shot DSC-HX50V has 20.4 megapixels on an 1/2.3" sensor having 70 MP/cm 2. The professional camera has a lower PPI than a compact camera, because it has larger photodiodes due to having far larger sensors.
A megapixel (MP) is a million pixels; the term is used not only for the number of pixels in an image but also to express the number of image sensor elements of digital cameras or the number of display elements of digital displays. For example, a camera that makes a 2048 × 1536 pixel image (3,145,728 finished image pixels) typically uses a few ...
Image scaling can be interpreted as a form of image resampling or image reconstruction from the view of the Nyquist sampling theorem.According to the theorem, downsampling to a smaller image from a higher-resolution original can only be carried out after applying a suitable 2D anti-aliasing filter to prevent aliasing artifacts.
Like any resampling operation, changing image size and bit depth are lossy in all cases of downsampling, such as 30-bit to 24-bit or 24-bit to 8-bit palette-based images. While increasing bit depth is usually lossless, increasing image size can introduce aliasing or other undesired artifacts.