Histogram Modification and Wavelet Transform for High Performance Watermarking

This paper studies the image fusion of high-resolution panchromatic image and low-resolution multispectral image. Based on the classic fusion algorithms on remote sensing image fusion, the PCA (principal component analysis) transform, and discrete wavelet transform, we carry out in-depth research. The compressed sensing (CS) abandons the full sample and shifts the sampling of the signal to sampling information that greatly reduces the potential consumption of traditional signal acquisition and processing. We combine compressed sensing with satellite remote sensing image fusion algorithm and propose an innovative fusion algorithm (CS-FWT-PCA), in which the symmetric fractional B-spline wavelet acts as the sparse base. In the algorithm we use Hama Da matrix as the measurement matrix and SAMP as the reconstruction algorithm and adopt an improved fusion rule based on the local variance. The simulation results show that the CS-FWT-PCA fusion algorithm achieves better fusion effect than the traditional fusion method.

Section: Cs-fwt-pca-based Satellite Remote Sensing Imagementioning

confidence: 99%

A Novel Algorithm for Satellite Images Fusion Based on Compressed Sensing and PCA

Yang¹,

Wang²,

Guo³

2013

“…Ni et al [22] realized the fundamental HS program and van Leest et al [23] expressed the similar thing. Later on, Ni et al 's HS scheme was extensively investigated and many improved works came into being [24]. Fallahpour and Sedaaghi [25] applied HS for specified blocks instead of the whole cover image.…”

Section: Introductionmentioning

confidence: 99%

Obtuse Angle Prediction and Factor Evaluation for Image Reversible Data Hiding

Xiang

Yuan

Hou

2017

A pixel-based pixel-value-ordering (PPVO) has been used for reversible data hiding to generate large embedding capacity and high-fidelity marked images. The original PPVO invented an effective prediction strategy in pixel-by-pixel manner. This paper extends PPVO and proposes an obtuse angle prediction (OAP) scheme, in which each pixel is predicted by context pixels with better distribution. Moreover, for evaluating prediction power, a mathematical model is constructed and three factors, including the context vector dimension, the maximum prediction angle, and the current pixel location, are analyzed in detail. Experimental results declare that the proposed OAP approach can achieve higher PSNR values than PPVO and some other state-of-the-art methods, especially in the moderate and large payload sizes.

“…Three categories of solutions can be summarized to this problem, difference expansion (DE) [8][9][10], histogram shifting (HS) [11][12][13][14], and integer transformation [15][16][17][18][19]. Difference expansion computes and expands the differences between pixels rather than the pixel itself to embed data.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO

Xiang

Yuan

Hou

2016

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.