What is bpp (bits per pixel)? What is the exact definition?
Answer:
*The terminology for image formats can be confusing because there are often several ways of describing the same format. This topic explains what the terms mean.
If an image is 24 bits per pixel, it is also called a 24-bit image, a true color image, or a 16M color image. Sixteen million is roughly the number of different colors that can be represented by 24 bits, where there are 8 bits for each of the red, green, and blue (RGB) values.
A 32-bit image is a specialized true-color format used in image files, where the extra byte carries information that is either converted or ignored when the file is loaded. The extra byte is used for an additional color plane in CMYK files, which are specialized files for color printing. In that case, LEADTOOLS, by default, converts the values to 24-bit RGB values when loading the image. The additional byte may also be used for an Alpha channel, which carries extra information such as a transparency indicator.
If an image is 16 bits per pixel, it is also called a 16-bit image, a high color image, or a 32K color image. Thirty-two thousand is roughly the number of different colors that can be represented by 16 bits, where there are 5 bits for each of the red, green, and blue values. (Devices that specify 64K color support are also referring to 16-bit images, but they are counting the left-over bit.)
If an image is 8 bits per pixel, it is also called an 8-bit image or a 256-color image. Two hundred fifty-six is the number of different colors that can be achieved by using the image data as 8-bit indexes to an array of colors called a palette.
If an image is 4 bits per pixel, it is also called a 4-bit image or a 16-color image. Sixteen is the number of different colors that can be achieved by using the image data as 4-bit indexes to a palette.
If an image is 1 bit per pixel, it is also called a 1-bit image, a black and white image, a 2-color image, or a bitonal image. Two is the number of different colors that can be achieved by using the image data as 1-bit indexes to a palette. The palette can contain colors other than black and white, although black and white are most common.
If an image is grayscale, its red, green, and blue values are all the same, and the values are incremented from the lowest to the highest. For example, an 8-bit grayscale image has 256 shades of gray, with values from 0 to 255.
*Large images consume large memory and make our computers struggle. Memory cost for an image is computed from the image size.
For a 6x4 inch image at 150 dpi, the image size is calculated as:
(6 inches × 150 dpi) × (4 inches × 150 dpi) = 900 × 600 pixels
900 × 600 pixels is 900 × 600 = 540,000 pixels.
The memory cost for this RGB color image is:
900 × 600 × 3 = 1.6 million bytes.
The last "× 3" is for 3 bytes of RGB color information per pixel for 24 bit color (3 RGB values per pixel, one 8-bit byte for each RGB value, which totals 24 bit color).
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[An image processor comprising:
a buffer memory for storing original colored image data having a plurality of screens, wherein each screen comprises data for one color of the original colored image data, the buffer memory having an output;
an encoder for fixed-length block compression, the encoder compressing blocks of the original colored image data at a compression ratio of 2 to the power of N (N is a positive integer greater than or equal to 2), wherein each block comprises a plurality of pixels and wherein the compression ratio is defined by the number of uncompressed pixels divided by the number of compressed pixels, and wherein the encoder is connected to the output of the buffer memory and further, the encoder outputs coded data through an output;
a coding memory having a capacity defined by one screen of the original image data; and
a write selector connected to the output of the buffer memory and to the output of the encoder, for selecting between the original colored image data outputted from the buffer memory or the coded colored data outputted from the encoder and storing the selected data in the coding memory;
wherein the coding memory stores either the coded data of plural colors from among the coded data of one screen when the write selector selects the encoder, or the coding memory stores one screen of the original data when the write selector selects the buffer memory.
2. An image processor according to claim 1, wherein the compression rate of said coding means is four, and said coding memory is capable of storing the coded data of all of red, green and blue colors from among the coded data of one screen when said write selector selects the coded data.
3. An image processor according to claim 1, wherein the compression rate of said coding means is four, and said coding memory is capable of storing the coded data of all of cyan, magenta and yellow colors from among the coded data of one screen when said write selector selects the coded data.
4. An image processor according to claim 3, wherein said coding memory is capable of storing the coded data of black color out of the coded data of the one screen when said write selector selects the coded data.
5. An image processor according to claim 1, wherein said coding means comprises:
(a) dividing means for dividing the original image data into small areas, each being a predetermined coding unit;
(b) reference level setting means for setting a reference level to be a reference when designating plural representative tonal levels representing the tonal levels of the individual pixels in each small area;
(c) differential value setting means for setting a differential value representing the difference between each of the representative tonal levels in the individual small area and the reference level; and
(d) resolution component value setting means for setting, for each of the pixels in the individual small area, a resolution component value representing as to which pixel is associated with which one of the plural representative tonal levels;
(e) said coded data being the reference level, the differential value and the resolution component value for every small area.
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