Example 5.26 Problem Example 5.26 Solution At 24 bits (3 bytes) per pixel, a 10:
ID: 1716429 • Letter: E
Question
Example 5.26 Problem Example 5.26 Solution At 24 bits (3 bytes) per pixel, a 10:1 image compression factor yieldsmagesize.m image files with B 0.3xY bytes. Find the expected value E[B] and the S 0.3(Sx.9T7), PMF PE b) imagepmf; SB-0.3 (SX. *SY) eb-sum (sum(SB. *PXY)) sb-unique (SB) pb-finitepaf (SB,PXY,sb),1 The script imagesize.m produces the expected value as eb, and produces the PMF, which is represented by the vectors sb and pb. The 3x3 matrix SB has ijth element g(xi, yj) = 0.3ziUj. The calculation of eb is simply a Matlab imple- PxY mentation of Theorem 5.9. Since some elements of SB are identical, sb-unique (SB) extracts the unique elements. Although SB and PxY are both 3 × 3 matrices, each is stored internally by Matlab as a 9-element vector. Hence, we can pass SB and PXY to the finitepmf ) function, which was designed to handle a finite random variable described by a pair of column vectors. Figure 5.7 shows one result of running the pro- gram imagesize. The vectors sb and pb comprise PEb. For example, PB(288000) = 0.3.Explanation / Answer
Small versus large pixels do not control the noise. Rather it is the lens that delivers the light. small sensors generally have smaller pixels and smaller lenses. It is the smaller lens that delivers a smaller amount of light to those small pixels. A small pixel can not collect as much light as a large pixel. The large pixel enables one to take higher ISO and still maintain higher image quality over the small pixel because a larger lens is used which delivers more light in the same exposure time. In fact, for the in this test one needs to go to extreme differences in ISO between the small and large pixel where ISO 100 images from the small pixel show similar image quality to ISO 1600 images from the large pixel. This large difference is accurately described by the "Unity Gain ISO" .
yes, it is a good image compression
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