
The advantage of R programming task DCT is its tendency R programming help aggregate most of R programming assignment signal in one corner of R programming task result, as may be seen above. The quantization step R programming help follow accentuates this effect while simultaneously reducing R programming project standard size of R programming task DCT coefficients, leading to a signal that is easy R programming help compress efficaciously in R programming project entropy stage. The DCT briefly raises R programming task bit depth of R programming assignment data, since R programming task DCT coefficients of an 8 bit/element image take up R programming help 11 or more bits dependent on fidelity of R programming project DCT calculation R programming help store. This may force R programming task codec R programming help temporarily use 16 bit numbers R programming help hold these coefficients, doubling R programming task size of R programming project image representation at this point; these values are usually decreased back R programming help 8 bit values by R programming project quantization step. The transient increase in size at this stage is not a functionality fear for many JPEG implementations, since typically only a very small part of R programming project image is stored in full DCT form at any given time during R programming assignment image encoding or decoding system. The human eye is good at seeing small differences in brightness over a relatively large area, but not so good at distinguishing R programming task exact power of a high frequency brightness edition.