Enhanced arithmetic coding using total frequency in power of 2 & processing multi-bits at a time

The purpose of the research work presented here is to reduce the execution time of Arithmetic Coding. Arithmetic coding is a very efficient and most popular entropy coding technique used with most of the data compression methods. Arithmetic coding is based on cumulative probability of symbols. In implementations using integer arithmetic, it uses cumulative frequency/total frequency in place of cumulative probability while computing new subinterval (low, high). In renormalization loop, it outputs single matching most significant bit of low and high bound of an interval and renormalizes an interval till an interval becomes 2^b-2 wide, where b is number of bits used to store the range value. In this paper, multibit-power 2 approach is proposed that combines two techniques together to increase the speed of execution. One technique is to scale all frequencies such that total frequency results in power of two. This enables division or multiplication by total frequency to be performed using shift operations. Another technique is to process more than one bit at a time to reduce the number of iterations of renormalization loop. As compared to conventional implementations, proposed multibit-power 2 approach has resulted in 82% overall saving in the execution speed while compressing with no compromise on compression rate and 38% overall gain in execution time while decompressing.