 to as unary coding. For instance, the numbers n = 2, n = 5, and n = 7 are represented in unary coding as 01, 00001, and 0000001, respectively. This definition should not be confused with the "unary number representation," which uses only one symbol character (e.g., 1) and is defined according to the rule 0 decimal = = 11 unary 2 decimal = 111umary, etc. 10.2 Arithmetic coding most codewords in the list. If we apply the Golomb code to the English-symbol source (Table 8.3), we obtain mean codeword lengths of L = 4.465 bit/word (first, simple definition with m : 8), L = 4.316 bit/word (second, actual definition with m = 6), corresponding to efficiencies (n = H/L = 4.184/L) of n = 93.69% n = 96.94%, respectively. These two results represent a significant improvement on the previous Elias (gamma or delta) codes (n = 79.83%) and second-order Fibonacci codes (n = 84.90%). It is clear that the Golomb-code parameter m must be optimized according to the source size and distribution type. Golomb codes with low m have relatively small codewords for the first few integers (owing to the short suffix of length log2 m), but the length rapidly increases because of the fast prefix increment. On the contrary, Golomb codes with high m have relatively large codewords for the first few integers (owing to the long suffix), but the length increases slowly because of the slow prefix increment. Golomb codes with m = 2k (k an integer) have also been known as Rice codes. For this reason one generally refers to Golomb-Rice codes to designate them altogether (with m = 2k corresponding to Rice codes). For sources of specific distribution types, it is possible to determine the optimal exponent parameter k that minimizes the mean codeword length. In the general case, this parameter can also be determined through heuristic methods. Integer coding based on various Elias or Golomb-Rice derivatives finds many applica- tions in the field of database management, ensuring rapid access to and optimal indexing of library files. An illustrative applicati