Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO
Most pixel-value-ordering (PVO) predictors generated prediction-errors including −1 and 1 in a block-by-block manner. Pixel-based PVO (PPVO) method provided a novel pixel scan strategy in a pixel-by-pixel way. Prediction-error bin 0 is expanded for embedding with the help of equalizing context pixel...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2016/2585983 |
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doaj-1d6bf04a61ef44e0838fcc30db02f4f52020-11-24T23:16:13ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472016-01-01201610.1155/2016/25859832585983Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVOHongyin Xiang0Jinsha Yuan1Sizu Hou2Institute of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071000, ChinaInstitute of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071000, ChinaInstitute of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071000, ChinaMost pixel-value-ordering (PVO) predictors generated prediction-errors including −1 and 1 in a block-by-block manner. Pixel-based PVO (PPVO) method provided a novel pixel scan strategy in a pixel-by-pixel way. Prediction-error bin 0 is expanded for embedding with the help of equalizing context pixels for prediction. In this paper, a PPVO-based hybrid predictor (HPPVO) is proposed as an extension. HPPVO predicts pixel in both positive and negative orientations. Assisted by expansion bins selection technique, this hybrid predictor presents an optimized prediction-error expansion strategy including bin 0. Furthermore, a novel field-biased context pixel selection is already developed, with which detailed correlations of around pixels are better exploited more than equalizing scheme merely. Experiment results show that the proposed HPPVO improves embedding capacity and enhances marked image fidelity. It also outperforms some other state-of-the-art methods of reversible data hiding, especially for moderate and large payloads.http://dx.doi.org/10.1155/2016/2585983 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hongyin Xiang Jinsha Yuan Sizu Hou |
spellingShingle |
Hongyin Xiang Jinsha Yuan Sizu Hou Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO Mathematical Problems in Engineering |
author_facet |
Hongyin Xiang Jinsha Yuan Sizu Hou |
author_sort |
Hongyin Xiang |
title |
Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO |
title_short |
Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO |
title_full |
Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO |
title_fullStr |
Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO |
title_full_unstemmed |
Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO |
title_sort |
hybrid predictor and field-biased context pixel selection based on ppvo |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2016-01-01 |
description |
Most pixel-value-ordering (PVO) predictors generated prediction-errors including −1 and 1 in a block-by-block manner. Pixel-based PVO (PPVO) method provided a novel pixel scan strategy in a pixel-by-pixel way. Prediction-error bin 0 is expanded for embedding with the help of equalizing context pixels for prediction. In this paper, a PPVO-based hybrid predictor (HPPVO) is proposed as an extension. HPPVO predicts pixel in both positive and negative orientations. Assisted by expansion bins selection technique, this hybrid predictor presents an optimized prediction-error expansion strategy including bin 0. Furthermore, a novel field-biased context pixel selection is already developed, with which detailed correlations of around pixels are better exploited more than equalizing scheme merely. Experiment results show that the proposed HPPVO improves embedding capacity and enhances marked image fidelity. It also outperforms some other state-of-the-art methods of reversible data hiding, especially for moderate and large payloads. |
url |
http://dx.doi.org/10.1155/2016/2585983 |
work_keys_str_mv |
AT hongyinxiang hybridpredictorandfieldbiasedcontextpixelselectionbasedonppvo AT jinshayuan hybridpredictorandfieldbiasedcontextpixelselectionbasedonppvo AT sizuhou hybridpredictorandfieldbiasedcontextpixelselectionbasedonppvo |
_version_ |
1725588280265670656 |