Lecture 13
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Digital color images are characterized by 3 or 4 dimensional vectors representing pixels in a color space model like RGB or CMYK. RGB uses additive color mixing of red, green and blue light, while CMYK uses subtractive color mixing of cyan, magenta, yellow and black inks. Color images can be processed by manipulating individual color channels separately or by extracting and processing luminosity from the RGB channels. Transforming between RGB and luminosity (YUV) space allows separate processing and recombination of luminosity and chromatic information. YUV space is also used in JPEG compression by applying higher compression rates to the chromatic channels U and V.
Lecture 13
- 1. PROCESSING OF COLOR IMAGES CS-467 Digital Image Processing 1
- 2. Digital color images • A pixel of a digital color image is characterized not by an intensity value, but by a 3- or 4- dimensional vector, representing the pixel in a corresponding color space • A color space is an abstract mathematical model describing the way colors can be represented as tuples of numbers (typically as 3 or 4 values) 2
- 3. RGB color space • RGB (Red Green Blue) uses additive color mixing, because it describes what kind of light needs to be emitted to produce a given color. • Additive color mixing: Three overlapping light bulbs in a vacuum, adding together to create white 3
- 4. CMYK color space • CMYK (Cyan Magenta Yellow Key) uses subtractive color mixing used in the printing process, because it describes what kind of inks need to be applied so the light reflected from the substrate and through the inks produces a given color. • Subtractive color mixing: Three splotches of paint on white paper, subtracting together to turn the paper black 4
- 5. RGB vs. CMYK • A comparison of RGB and CMYK color models. This image demonstrates the difference between how colors will look on a computer monitor (RGB) compared to how they will reproduce in a CMYK print process 5
- 6. RGB vs. CMYK • CMYK color space is used for color printing (the K component characterizes specific properties of a particular printer) • RGB color space is used for storing color images and their processing (filtering, enhancement) 6
- 7. Processing of color images • In the RGB space, its components measure the intensity and chrominance of light • The actual information stored in the digital image data is the intensity information in each spectral band • If a digital color image is represented using 8 bit/component precision, then the 24-bit RGB model may represent 256×256×256 ≈ 16.7 million colors 7
- 8. Processing of color images • Any kind of color image processing can be implemented in two alternative ways: Each color channel can be processed separately, using even different filters with different parameters. Moreover, one or two color channels may remain unprocessed A luminosity information can be extracted from an RGB image; then, after its processing, it can be inserted back into the image 8
- 9. Processing of color images • Separate processing of color channels is reasonable if, for example these channels are corrupted by different noises or if it is necessary to correct or enhance colors • A luminosity channel can be processed if only the luminosity information is corrupted 9
- 10. RGB/Luminosity transformation • RGB pixel intensities can be transformed to the YUV space as follows • Where Y is a luminosity information (channels), U and V are chromatic channels 10 0.299 0.587 0.114 0.14713 0.28886 0.436 0.615 0.51499 0.10001 Y R U G V B =− − − −
- 11. Luminosity/RGB transformation • RGB pixel intensities can be restored from the YUV space as follows • Where Y is a luminosity information (channels), U and V are chromatic channels 11 1 0 1.13983 1 0.39465 0.5806 1 2.03211 0 R Y G U B V =− −
- 12. YUV space and JPEG compression • YUV space is used in JPEG image compression • To apply JPEG compression, an RGB image shall be transformed into an YUV image • Then, JPEG compression is applied to the YUV components. This makes it possible to reach a higher compression rate, because the U and V channels can be easier compressed with a significantly higher rate (up to 90% without losing significant information) rather than any of RGB components 12