 entropy Mean block length 3.570 bit/word Efficiency 98.65% 9.3 Block codes or = 1.76 bit/symbol and for the coding efficiency, n 2 100%. This example shows that one can reach the theoretical limit of 100% coding efficiency with arbitrary accuracy, but at the price of using an extended dictionary of codewords, most with relatively long lengths. Example 9.2: 26-event source; the English-language characters Block coding may not be SO practical when applied to sources having more than two events. This is because Huffman coding is very close to being the most efficient, and the extra complexity introduced by using an extended source with a long list of variable- length codewords is not SO much worth it. To illustrate this point, consider the case of the English language. For simplicity, it can be viewed as a 26-event source, namely producing the A-Z symbol characters, which we call X Superscript(1). The key question we want to address here is: "Could one use a different alphabet and its associated block code to convey more information in any length of text?" Indeed, is it possible to squeeze a piece of English text by means of a super-alphabet? One may conceive of such a super-alphabet as being made from character pairs (also called digrams), representing altogether 26 X 26 = 676 new symbol characters. Thus, all English books and written materials using this super-alphabet would be twice as short as the originals! This would reduce their production costs, their price and weight, and possibly they could be faster to read. But such improvements would be at the expense of having to learn and master the different characters. 10 Here, the point is not to propose changing the English alphabet, but rather to analyze how text information could be compressed for saving memory space and speeding up transmission between computers. To build this new code, we ought to assign the shortest codewords to the most frequently used digrams, 10 Two illustrative examples of "super-alphabets" are the symbolic/ideographic/logogram kanjis of