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Subsampling the color components horizontally by 4 yields 4:1:1 sampling With 4:1:1 sampling, each pixel has its own unique luminance component value but shares its two chrominance component values with three other horizontally adjacent pixels In other words, pixels 0, 1, 2, and 3 in a row share the same U and V component values, while they have unique Y values This format has an effective bandwidth of 12 bits per pixel A final form of subsampling involves subsampling the color components by a factor of 2 in both the horizontal and vertical directions Thus, in a quad of four pixels, each pixel has its own unique luminance component value but shares its two chrominance component values with the other pixels in the quad This type of subsampling is called 4:2:0 sampling Like 4:1:1 sampling, the 4:2:0 format also has an effective bandwidth of 12 bits per pixel The standard for television video (CCIR-601) requires 4:2:2 sampling Thus, practically all capture cards that perform an analog-to-digital

f(x)

Test the movie and drag the mask around to reveal what is underneath!

1111111111111111111111111 0000000000000000000000000 Feasible space 1111111111111111111111111 0000000000000000000000000 1111111111111111111111111 0000000000000000000000000 1111111111111111111111111 0000000000000000000000000 1111111111111111111111111 0000000000000000000000000 1111111111111111111111111 0000000000000000000000000 1111111111111111111111111 0000000000000000000000000 1111111111111111111111111 0000000000000000000000000 1111111111111111111111111 0000000000000000000000000 1111111111111111111111111 0000000000000000000000000 Extremum 1111111111111111111111111 0000000000000000000000000 Infeasible space 1111111111111111111111111 0000000000000000000000000 1111111111111111111111111 0000000000000000000000000 1111111111111111111111111 0000000000000000000000000 1111111111111111111111111 0000000000000000000000000 1111111111111111111111111 0000000000000000000000000 1111111111111111111111111 0000000000000000000000000 1111111111111111111111111 0000000000000000000000000 1111111111111111111111111 0000000000000000000000000

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Figure A3 Constrained Problem Illustration How should two feasible solutions be compared The answer to this question is somewhat obvious: the solution with the better objective function value should be preferred How should two infeasible solutions be compared In this case the answer is not at all obvious, and is usually problem-dependent The issues to consider are: should the infeasible solution with the best objective function value be preferred should the solution with the least number of constraint violations or lowest degree of violation be preferred should a balance be found between best objective function value and degree of violation Should it be assumed that any feasible solution is better than any unfeasible solution Alternatively, can objective function value and degree of violation be optimally balanced Again, the answer to this problem may be problemdependent In nancial-critical, or life-critical problems, the rst strategy should be preferred to ensure no nancial loss, or loss of life Less critical problems, such as time-tabling, may consider solutions where less severe constraints are violated Research in constraint handling methods are numerous in the evolutionary computation (EC) and swarm intelligence (SI) paradigms Based on these research e orts, constraint handling methods have been categorized in a number of classes [140, 487, 584]:

Initialize the codebook vectors for a small, undersized SOM; while stopping condition(s) not true do while growing condition not triggered do Train the SOM for t pattern presentations using any SOM training method; end if grow condition is met then Find the neuron kj with the largest quantization error; Find the furthest immediate neighbor mn in the row-dimension of the map, and the furthest neuron rs in the column-dimension; Insert a column between neurons kj and rs and a row between neurons kj and mn (this step preserves the square structure of the map); For each neuron ab in the new column, initialize the corresponding codebook vectors wab using (437) wab = (wa,b 1 + wa,b+1 ) and for each neuron in the new row, wab = (wa 1,b + wa+1,b ) (438)

(A/D) conversion on their composite, S-Video, or component inputs produce a resulting 4:2:2 digital video stream They also typically quantize each component into 8 bits, although some solutions do generate 10 bits per component

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where (0, 1) end end Re ne the weights of the nal SOM architecture with additional training steps until convergence has been reached

rather expressed as PT , with (0, 1) Ultimately, the map size should be at least equal to the number of independent variables in the training set The optimal value of is problem dependent, and care should be taken to ensure that is not too small if a growing SOM is not used If this is the case, the nal map may not converge to the required quantization error since the map size will be too small

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