A Clustering-Based Framework for Improving the Performance of JPEG Quantization Step Estimation

Jianquan YANG, Yulan ZHANG, Guopu ZHU, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

19 Citations (Scopus)


Quantization plays a pivotal role in JPEG compression with respect to the tradeoff between image fidelity and storage size, and the blind estimation of quantization parameters has attracted considerable interest in the fields of image steganalysis and forensics. Existing estimation methods have made great progress, but they usually suffer a sharp decline in accuracy when addressing small-size JPEG decompressed bitmaps due to the insufficiency of coefficients. Aiming to alleviate this issue, this paper proposes a generic clustering-based framework to improve the performance of the existing methods. The core idea is to gather as many coefficients as possible by clustering subbands before feeding them into a step estimator. The proposed framework is implemented using hierarchical clustering with two kinds of histogram-like features. Extensive experiments are conducted to validate the effectiveness of the proposed framework on a variety of images of different sizes and quality factors, and the results show that notable improvements can be achieved. In addition to quantization step estimation, we believe the idea behind the proposed framework might provide inspiration for other forensic tasks to alleviate their performance issues induced by sample insufficiency.
Original languageEnglish
Pages (from-to)1661-1672
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number4
Early online date19 Jun 2020
Publication statusPublished - Apr 2021
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 61802382, Grant 61872350, Grant 61572489, and Grant 61672443, in part by the Hong Kong GRF-RGC General Research Fund under Grant 9042322 (CityU 11200116), Grant 9042489 (CityU 11206317), and Grant 9042816 (CityU 11209819), in part by the Tip-top Scientific and Technical Innovative Youth Talents of Guangdong Special Support Program under Grant 2019TQ05X696, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2020A1515010640, and in part by the Basic Research Program of Shenzhen under Grant JCYJ20170818163403748.


  • DCT coefficient analysis
  • hierarchical clustering
  • image forensics
  • JPEG compression
  • Quantization step estimation


Dive into the research topics of 'A Clustering-Based Framework for Improving the Performance of JPEG Quantization Step Estimation'. Together they form a unique fingerprint.

Cite this