Image Steganography Using Gradient Adjacent Prediction in Side-Match Vector Quantization

Shiau Rung Tsui, Cheng Ta Huang, Wei Jen Wang

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


This study presents a new steganographic method that embeds secret data into a cover digital image using VQ encoding. The core concept of the proposed method uses the gradient adjacent prediction (GAP) algorithm, which enhances prediction accuracy of neighboring blocks in SMVQ encoding. To embed secret data into the cover image, the proposed method utilizes the features of GAP to decide the capacity of the secret data per pixel in a block. It then embeds the secret data accordingly. It also embeds an index value in each block to ensure that the secret data can be recovered back. The index value points to the closest codeword of a state codebook to the encoding block, where the state codebook is generated by GAP-based SMVQ. The result shows that the proposed method has better performance than a recent similar work proposed by Chen and Lin in 2010.

Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Applications - Volume 2
Subtitle of host publicationProceedings of the International Computer
EditorsChang Ruay-Shiung, Peng Sheng-Lung, Lin Chia-Chen
Number of pages9
StatePublished - 2013

Publication series

NameSmart Innovation, Systems and Technologies
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026


  • Gradient Adjacent Prediction (GAP)
  • SMVQ
  • Steganography
  • VQ


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