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The total size of the compressed block is now 16 bits for the luminance bitmap, and two 24-bit binary quantities for each representative color, yielding a total size of 64 bits, which, when divided by 16 (the number of pixels in the block), yields 4 i.e. 4 bits per pixel. [1] [2] [3]
[1] [2] [3] Modern standards tend to use bits per component, [1] [2] [4] [5] but historical lower-depth systems used bits per pixel more often. Color depth is only one aspect of color representation, expressing the precision with which the amount of each primary can be expressed; the other aspect is how broad a range of colors can be expressed ...
The highest quality image below (Q=100) is encoded at nine bits per color pixel, the medium quality image (Q=25) uses one bit per color pixel. For most applications, the quality factor should not go below 0.75 bit per pixel (Q=12.5), as demonstrated by the low quality image.
Each sublevel is defined by a nominal bit-per-pixel (Nbpp) value that indicates the maximum amount of bits per pixel for an encoded image of the maximum permissible number of sampling grid points according to the selected conformance level.
The DSC standard supports up to a 3∶1 compression ratio (reducing the data stream to 8 bits per pixel) with constant or variable bit rate, RGB or Y′C B C R 4:4:4, 4:2:2, or 4:2:0 color format, and color depth of 6, 8, 10, or 12 bits per color component.
The size of raster image files is positively correlated with the number of pixels in the image and the color depth (bits per pixel). Images can be compressed in various ways, however. A compression algorithm stores either an exact representation or an approximation of the original image in a smaller number of bytes that can be expanded back to ...
Such files can be encoded in 12, 16 or 24 bits per pixel. Depending on subsampling, the formats can largely be described as 4:4:4, 4:2:2, and 4:2:0p. The apostrophe after the Y is often omitted, as is the "p" (for planar) after YUV420p.
Image compression is a type of data compression applied to digital images, to reduce their cost for storage or transmission. Algorithms may take advantage of visual perception and the statistical properties of image data to provide superior results compared with generic data compression methods which are used for other digital data.