Prediction-error of Prediction Error (PPE)-based Reversible Data Hiding

This paper presents a novel reversible data hiding (RDH) algorithm for gray-scaled images, in which the prediction-error of prediction error (PPE) of a pixel is used to carry the secret data. In the proposed method, the pixels to be embedded are firstly predicted with their neighboring pixels to obtain the corresponding prediction errors (PEs). Then, by exploiting the PEs of the neighboring pixels, the prediction of the PEs of the pixels can be determined. And, a sorting technique based on the local complexity of a pixel is used to collect the PPEs to generate an ordered PPE sequence so that, smaller PPEs will be processed first for data embedding. By reversibly shifting the PPE histogram (PPEH) with optimized parameters, the pixels corresponding to the altered PPEH bins can be finally modified to carry the secret data. Experimental results have implied that the proposed method can benefit from the prediction procedure of the PEs, sorting technique as well as parameters selection, and therefore outperform some state-of-the-art works in terms of payload-distortion performance when applied to different images.Comment: There has no technical difference to previous versions, but rather some minor word corrections. A 2-page summary of this paper was accepted by ACM IH&MMSec'16 "Ongoing work session". My homepage: hzwu.github.i

Progresive Error Prediction Sebagai Metode Filtering Data Curah Hujan Di Karangploso, Malang

Curah hujan merupakan salah satu faktor yang mempengaruhi cuaca, sehingga diperlukan data yang valid untuk dijadikan input pada peramalan cuaca. Dengan demikian diperlukan suatu metode untuk memfilter data tersebut agar dapat digunakan dalam input peramalan curah hujan. Metode yang sering digunakan untuk filtering data curah hujan yaitu metode moving average. Metode alternatif yang dapat digunakan untuk filter curah hujan dalam komputasi yaitu metode progresive error prediction. Progresive error prediction merupakan metode prediksi numerik dan pengembangan metode genetic algorithm, dengan ide pendekatan yaitu mengarahkan nilai error ke arah nol. Hasil filtering data curah hujan menunjukkan trend data curah hujan yang terfilter dengan menggunakan metode PEP lebih konsisten dan smooth dari pada metode MA, sehingga trend data curah hujan yang terfilter dengan menggunakan metode PEP dapat digunakan untuk memprediksi trend data curah hujan di waktu yang akan datang

Nonparametric estimation of mean-squared prediction error in nested-error regression models

Nested-error regression models are widely used for analyzing clustered data. For example, they are often applied to two-stage sample surveys, and in biology and econometrics. Prediction is usually the main goal of such analyses, and mean-squared prediction error is the main way in which prediction performance is measured. In this paper we suggest a new approach to estimating mean-squared prediction error. We introduce a matched-moment, double-bootstrap algorithm, enabling the notorious underestimation of the naive mean-squared error estimator to be substantially reduced. Our approach does not require specific assumptions about the distributions of errors. Additionally, it is simple and easy to apply. This is achieved through using Monte Carlo simulation to implicitly develop formulae which, in a more conventional approach, would be derived laboriously by mathematical arguments.Comment: Published at http://dx.doi.org/10.1214/009053606000000579 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

An efficient technique of texture representation in segmentation-based image coding schemes

In segmentation-based image coding techniques the image to be compressed is first segmented. Then, the information is coded describing the shape and the interior of the regions. A new method to encode the texture obtained in segmentation-based coding schemes is presented. The approach combines 2-D linear prediction and stochastic vector quantization. To encode a texture, a linear predictor is computed first. Next, a codebook following the prediction error model is generated and the prediction error is encoded with VQ. In the decoder, the error image is decoded first and then filtered as a whole, using the prediction filter. Hence, correlation between pixels is not lost from one block to another and a good reproduction quality can be achieved.Peer ReviewedPostprint (published version