Improved Efficiency Adaptive Arithmetic Coding for GPU with Lower Shared Memory Usage

Abstract

Arithmetic coding (AC) is widely used for lossless data compression, and parallelization of arithmetic coding is relatively simple because all symbols can be encoded independently. On the other hand, Parallel adaptive arithmetic coding stores the data model for each data block (the model contains the data structure used to calculate the probability distribution of the data) and these data models need to be frequently accessed and modified during encoding and decoding, so the access latency and computational complexity of the data model are important factors affecting performance. In this paper, we present a new data structure called partial prefix sum array which can quickly calculate the probability distribution of the data, so that parallel adaptive arithmetic encoding and decoding can be accelerated. Furthermore, we use a mode of coalescing access to access global memory, thereby improving the throughput of global memory. Experimental results for 6 files on NVIDIA Tesla M60 GPU show that our GPU adaptive arithmetic encoding and decoding run 1.61x- 2.75x times and 1.03x-2.22x times faster than previously presented GPU implementation, respectively.