Improving of entropy adaptive on-line compression

Since energy efficiency, high bandwidth, and low transmission delay are challenging issues in mobile networks, due to resource constraints, there is a great importance in designing of new communication methods. In particular, lossless data compression may provide high performance under constrained resources. In this paper we present a novel on-line and entropy adaptive compression scheme for streaming unbounded length inputs. The scheme extends the window dictionary Lempel–Ziv compression and is adaptive and tailored to compress on-line non entropy stationary inputs. Specifically, the window dictionary size is changed in an adaptive manner to fit the current best compression rate for the input. On-line entropy adaptive compression scheme (EAC), introduced and analyzed in this paper, examines all possible sliding window sizes over the next input portion to choose the optimal window size for this portion; a size that implies the best compression ratio. The size found is then used in the actual compression of this portion. We suggest an adaptive encoding scheme, which optimizes the parameters block by block, and base the compression performance on the optimality proof of LZ77 when applied to blocks (Ziv in IEEE Trans Inf Theory 55(5):1941–1944, 2009). This adaptivity can be useful for many communication tasks. In particular, providing efficient utilization of energy consuming wireless devices by data compression. Due to the dynamic and non-uniform structure of multimedia data, adaptive approaches for data processing are of special interest. The EAC scheme was tested on different types of files (docx, ppt, jpeg, xls) and over synthesized files that were generated as segments of homogeneous Markov Chains. Our experiments demonstrate that the EAC scheme typically provides a higher compression ratio than LZ77 does, when examined in the scope of on-line per-block compression of transmitted (or compressed) files. We propose techniques intended to control the adaptive on-line compression process by estimating relative entropy between two sequential blocks of data. This approach may enhance performance of the mobile networks.