Autonomous Parameter Adjustment Method for Lossless Data Compression on Adaptive Stream-Based Entropy Coding

Shinichi Yamagiwa,Suzukaze Kuwabara

doi:10.1109/access.2020.3029705

Shinichi Yamagiwa, Suzukaze Kuwabara

Open Access

https://doi.org/10.1109/access.2020.3029705

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Abstract

As speed of communication data path is drastically improved in this decade due to the high data rate, evolutional technology is demanded to address the fast communication implementation. In this paper, we focus on data compression technology to speed up the communication data path. We have proposed a stream-based data compression called ASE coding. It compresses data stream based on the instantaneous data entropy without buffering and stalling for the compression processes. It is also suitable for hardware implementation. However, the stream-based data compression works heuristically with sensitive parameters that affect to the data compression ratio. If the parameters are statically configured, it does not follow the dynamic data entropy, and thus, the data compression performance becomes unstable. In this paper, we will disseminate the parameters, discuss the behaviors of those parameters and propose its autonomous adjustment methods. We will also propose adjustment algorithms for those parameters that follow the data entropy of the input data stream autonomously. Through experimental evaluations applying the algorithms, we will confirm the parameters are adjusted with depending on the data entropy in the data stream. And then, the compression ratio becomes stable as the compressor exploits the minimal entropy adaptively.

Highlights

Due to the rapid growth of information systems, generation of data is getting drastically promoted
This paper proposes a novel technique to implement a stream-based data compression method with autonomous configuration for the parameters related to compression ratio
ADAPTIVE STREAM-BASED ENTROPY CODING To overcome the potential drawback of LCA-DLT that the compressed symbol is fixed size, we have developed a new stream-based data compression mechanism called Adaptive Stream-based Entropy (ASE) Coding [10], [11]

Summary

INTRODUCTION

Due to the rapid growth of information systems, generation of data is getting drastically promoted. VOLUME 8, 2020 from the number of occupied entries in the look-up table at the timing when a symbol is received by the compressor It works faster and provides 10 times smaller implementation on FPGA than the previous version of our stream-based data compression method [12]. This paper proposes a novel technique to implement a stream-based data compression method with autonomous configuration for the parameters related to compression ratio. This technique eliminates manual adjustment for the parameters. Adaptive modification of the look-up table size to obtain high probability by matching input symbols to the registered entries for stream-based data compression These contributions are applicable to the conventional methods that use look-up table.

BACKGROUNDS AND DEFINITIONS

Findings

CONCLUSION