Watching Over

by Vian Esterhuizen

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A JPEG may start out with 8 bits per R, G and B channel, but when stored in the JPEG it is stored very differently, where there is no real "bit depth" but instead values are stored as frequency coefficients of a given precision. In JPEG what's more relevant is the quantization rate, which affects how much information is thrown away during the quantization ...


The YCbCr model has a few variations in different application contexts, but essentially they are all some affine (linear) transformation on the RGB color data. If you think of the RGB space in terms of a 3D cube, with its sides representing the R, G and B axes (for JPEG, their range would normally be from 0 to 255), then each pixel in your image corresponds ...


First thing to show up in Google was a link to a Gimp plugin called "Decompose". You'll probably see an equivalent for Photoshop. They have a corresponding recompose as well. In general, there are two concepts to keep in mind: Image "Mode" and Layers. The Image "Mode" has to do with whether the layers of your color image are split into RGB, Grayscale ...


There are a few formats for YCbCr. generally speaking the eye is more sensitive to changes in luminance (Y, brightness) than to changes in chroma (Cb, Cr, color). Thus, it is possible to erase some chroma information while retaining image quality. Thus, the most "expensive" format is 4:4:4, where for each luma (Y) component there are 1 Red difference (Cr) ...


Short answer, no. But here's some suggestions (in order from easiest to hardest) that might be useful to meet your end-goal of learning about the colour space. The eyedroppers link mentioned by @Stan has many apps that provide variously RGB, CMYK, HSV/HSB, HSL and conversions to various standard representations of those (e.g. for HTML). While that doesn't ...


Representing the chroma (Cb Cr) in separate channels from the luma (Y) has another positive effect on compression. Most of the visible information is in the luma channel. Human eyes tolerate both lower spatial resolution and more aggressive quantization in the chroma channels. So an aggressively compressed image can end up consuming about 10% of the file ...

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