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

Yamagiwa, Shinichi; Kuwabara, Suzukaze

doi:10.1109/access.2020.3029705

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Another style of data compression method is the lossless method that the original data are decoded from the compressed ones. Focusing on the lossless compression for data stream, the Adaptive Stream-based Entropy (ASE) coding has been developed [ 20 , 36 ]. It eliminates buffering any part of the data stream and any stall during compression/ decompression operations based on a look-up table.…”

Section: Background and Definitionsmentioning

confidence: 99%

Stream-Based Visually Lossless Data Compression Applying Variable Bit-Length ADPCM Encoding

Yamagiwa

Ichinomiya

2021

Sensors

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Video applications have become one of the major services in the engineering field, which are implemented by server–client systems connected via the Internet, broadcasting services for mobile devices such as smartphones and surveillance cameras for security. Recently, the majority of video encoding mechanisms to reduce the data rate are mainly lossy compression methods such as the MPEG format. However, when we consider special needs for high-speed communication such as display applications and object detection ones with high accuracy from the video stream, we need to address the encoding mechanism without any loss of pixel information, called visually lossless compression. This paper focuses on the Adaptive Differential Pulse Code Modulation (ADPCM) that encodes a data stream into a constant bit length per data element. However, the conventional ADPCM does not have any mechanism to control dynamically the encoding bit length. We propose a novel ADPCM that provides a mechanism with a variable bit-length control, called ADPCM-VBL, for the encoding/decoding mechanism. Furthermore, since we expect that the encoded data from ADPCM maintains low entropy, we expect to reduce the amount of data by applying a lossless data compression. Applying ADPCM-VBL and a lossless data compression, this paper proposes a video transfer system that controls throughput autonomously in the communication data path. Through evaluations focusing on the aspects of the encoding performance and the image quality, we confirm that the proposed mechanisms effectively work on the applications that needs visually lossless compression by encoding video stream in low latency.

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Section: Background and Definitionsmentioning

confidence: 99%

Stream-Based Visually Lossless Data Compression Applying Variable Bit-Length ADPCM Encoding

Yamagiwa

Ichinomiya

2021

Sensors

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“…The improved LZW algorithm has been applied to power fault data, and experimental results show that the method effectively reduces the mean square error rate. A stream-based data compression method, called ASE coding has been proposed in [19]. This method dynamically adjusts the influencing parameters by calculating the instantaneous entropy of the compressed data stream to obtain a more stable CR.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic and Parallel Two-Stage Lossless Data Compression Method for Smart Grid

Wu,

Xu,

et al. 2023

IEEE Access

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With the rapid development of smart grid (SG) technologies, massive system data has been generated for SG for grid operation status monitoring and fault warning, this massive integrated data has brought great challenges for data transmission and storage. Thus, the breakthrough of data compression technology has become more and more important for data processing, this proposal, a lossless data compression method is proposed. The proposed method improves the Lempel-Ziv-Welch (LZW) algorithm. Specifically, the proposed method employs a parallel search approach to search the index of the dictionary globally by dividing the dictionary into several small dictionaries of different sizes and bit widths. In addition, the dynamic variable-length coding method is used as the output code which can offer flexible bit widths instead of fixed values, and the optimal dictionary partition combination and size parameters are selected to construct dictionaries. At last, the improved algorithm is cascaded with Huffman algorithm to form the proposed data compression algorithm. The data compression efficiency has been successfully verified by comparing with conventional Huffman and LZW data compression algorithms through data simulation, and it is observed to offer a better compression ratio than those methods with only LZW algorithm or Huffman algorithm, and save storage space effectively. More than this, the proposed method has realized lossless compression for power system data and guaranteed the integrity of the data, which will have better applicability for dealing with any similar information data.INDEX TERMS Lossless data compression, smart grid, Huffman algorithm, LZW algorithm.

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

Cited by 2 publications

References 22 publications

Stream-Based Visually Lossless Data Compression Applying Variable Bit-Length ADPCM Encoding

Stream-Based Visually Lossless Data Compression Applying Variable Bit-Length ADPCM Encoding

A Dynamic and Parallel Two-Stage Lossless Data Compression Method for Smart Grid

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