Two-Component GMM Source Coding and Optimization

Dan Song; Jinkai Ren; Lin Wang; Huihui Wu; Jun Chen; Guanrong Chen

doi:10.1109/TITS.2025.3568227

Two-Component GMM Source Coding and Optimization

Dan Song^*
, Jinkai Ren
, Lin Wang
, Huihui Wu
, Jun Chen
, Guanrong Chen

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

6 Citations (Scopus)

Abstract

A protograph low-density parity-check (P-LDPC) code-based lossy coding system is proposed for compressing the Gaussian mixture model source, as a particular case of shipping transportation systems. The compression performance is benchmarked against the rate-distortion bounds for this source. Some effective methods are developed to improve both the encoder and the decoder to reduce the compression distortion. Experimental results demonstrate that the proposed methods achieve good performance while maintaining low complexity.

© 2025 IEEE. All rights reserved, including rights for text and data mining, and training of artificial intelligence and similar technologies. Personal use is permitted, but republication/redistribution requires IEEE permission.

Original language	English
Pages (from-to)	16090-16103
Number of pages	14
Journal	IEEE Transactions on Intelligent Transportation Systems
Volume	26
Issue number	10
Online published	16 May 2025
DOIs	https://doi.org/10.1109/TITS.2025.3568227
Publication status	Published - Oct 2025

Funding

This work was supported in part by the Natural Science Foundation of China under Grant 61671395 and Grant 61971365; in part by Fujian Provincial Natural Science Foundation of China under Grant 2024J01101 and Grant 2024J01120; in part by Fujian Province Young and Middle-Aged Teacher Education Research Project under Grant JAT231044; in part by the Key Science and Technology Project of Fujian Province under Grant 2024H6030; in part by the National Science Foundation of Xiamen, China, under Grant 3502Z202372013 and Grant 3502Z202473053; in part by the Open Project of the Key Laboratory of Underwater Acoustic Communication and Marine Information Technology (Xiamen University) of the Ministry of Education, China, under Grant UAC202304; and in part by the Jimei University Startup Research Project under Grant ZQ2024058, Grant ZQ2022015, Grant ZQ2024099, Grant ZQ2024092, and Grant ZQ2024045.

Research Keywords

Gaussian mixture model
Lossy source coding
optimal coding algorithm
P-LDPC codes
rate distortion function

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title = "Two-Component GMM Source Coding and Optimization",

abstract = "A protograph low-density parity-check (P-LDPC) code-based lossy coding system is proposed for compressing the Gaussian mixture model source, as a particular case of shipping transportation systems. The compression performance is benchmarked against the rate-distortion bounds for this source. Some effective methods are developed to improve both the encoder and the decoder to reduce the compression distortion. Experimental results demonstrate that the proposed methods achieve good performance while maintaining low complexity. {\textcopyright} 2025 IEEE. All rights reserved, including rights for text and data mining, and training of artificial intelligence and similar technologies. Personal use is permitted, but republication/redistribution requires IEEE permission.",

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author = "Dan Song and Jinkai Ren and Lin Wang and Huihui Wu and Jun Chen and Guanrong Chen",

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month = oct,

doi = "10.1109/TITS.2025.3568227",

language = "English",

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TY - JOUR

T1 - Two-Component GMM Source Coding and Optimization

AU - Song, Dan

AU - Ren, Jinkai

AU - Wang, Lin

AU - Wu, Huihui

AU - Chen, Jun

AU - Chen, Guanrong

PY - 2025/10

Y1 - 2025/10

N2 - A protograph low-density parity-check (P-LDPC) code-based lossy coding system is proposed for compressing the Gaussian mixture model source, as a particular case of shipping transportation systems. The compression performance is benchmarked against the rate-distortion bounds for this source. Some effective methods are developed to improve both the encoder and the decoder to reduce the compression distortion. Experimental results demonstrate that the proposed methods achieve good performance while maintaining low complexity. © 2025 IEEE. All rights reserved, including rights for text and data mining, and training of artificial intelligence and similar technologies. Personal use is permitted, but republication/redistribution requires IEEE permission.

AB - A protograph low-density parity-check (P-LDPC) code-based lossy coding system is proposed for compressing the Gaussian mixture model source, as a particular case of shipping transportation systems. The compression performance is benchmarked against the rate-distortion bounds for this source. Some effective methods are developed to improve both the encoder and the decoder to reduce the compression distortion. Experimental results demonstrate that the proposed methods achieve good performance while maintaining low complexity. © 2025 IEEE. All rights reserved, including rights for text and data mining, and training of artificial intelligence and similar technologies. Personal use is permitted, but republication/redistribution requires IEEE permission.

KW - Gaussian mixture model

KW - Lossy source coding

KW - optimal coding algorithm

KW - P-LDPC codes

KW - rate distortion function

UR - https://www.scopus.com/pages/publications/105005274303

UR - https://www.scopus.com/pages/publications/105005274303#tab=impact

U2 - 10.1109/TITS.2025.3568227

DO - 10.1109/TITS.2025.3568227

M3 - RGC 21 - Publication in refereed journal

SN - 1524-9050

VL - 26

SP - 16090

EP - 16103

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

IS - 10

ER -

Two-Component GMM Source Coding and Optimization

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