Space-Time Optimization in Natural Language Text Compression - Anisimov, Anatoly; Zavadskyi, Igor (PHYSICO-MATHEMATICAL MODELLING AND INFORMATIONAL TECHNOLOGIES, 2023)
In this paper, we discuss various problems arising in space and time optimization of natural language text compression methods. We define a new class of variable-length universal data compression codes with multiple delimiters — the Reverse Multi-Delimiter (RMD) codes. They are synchronizable, allow us to perform fast Boyer-Moore-style search in a compressed file, and at the same time provide the best compression ratio among all codes of a discussed class. In combination with a special technique of preprocessing a natural language text and its dictionary, they improve the performance of modern powerful achievers. Also, we construct a very fast decoding algorithm for RMD-codes operating almost at the same speed as (s,c)-dense codes and times faster than Fi-bonacci codes decoding. The provided experiments show that RMD-codes occupy a very attractive position by the means of space/decoding time tradeoffs in natural language text compression.