Subjectively adapted high capacity lossless image data hiding based on prediction errors

Abstract

Abstract-This article reports on a lossless data hiding scheme for digital images where the data hiding capacity is either determined by minimum acceptable subjective quality or by the demanded capacity. In the proposed method data is hidden within the image prediction errors, where the most well-known prediction algorithms such as the median edge detector (MED), gradient adjacent prediction (GAP) and Jiang prediction are tested for this purpose. In this method, first the histogram of the prediction errors of images are computed and then based on the required capacity or desired image quality, the prediction error values of frequencies larger than this capacity are shifted. The empty space created by such a shift is used for embedding the data. Experimental results show distinct superiority of the image prediction error histogram over the conventional image histogram itself, due to much narrower spectrum of the former over the latter. We have also devised an adaptive method for hiding data, where subjective quality is traded for data hiding capacity. Here the positive and negative error values are chosen such that the sum of their frequencies on the histogram is just above the given capacity or above a certain quality.

Reversible Information Hiding in Images Based on Histogram Shift Method

“…Table 2 presents the BPP and PSNR of the baboon image for each prediction method. (13) When PSNR is around 30, optimum hiding and extraction are enabled at the 10th level. For a complex image such as the baboon image, CHLR is regarded as the optimal method.…”

High capacity reversible data hiding with interpolation and adaptive embedding

“…We now briefly discuss the use of interpolation techniques in IRDH schemes. The main uses of interpolation are found for ( i ) computing prediction errors (PEE-based schemes [30–32]), ( ii ) computing embeddable bits (DLE-based schemes [23–25]), and ( iii ) a combination of both [27]. An IRDH scheme with PEE computes the predicted errors from the cover image and interpolated image, and the secret data are embedded by modifying the predicted errors in a subset of original pixels.…”

Block-based reversible data hiding with multi-round estimation and difference alteration

“…Besides taking the difference values directly between neighboring pixels, by use of prediction schemes for producing the predicted image, and then by taking the error between predicted and original images for producing the difference histogram would also be a practical means for reversible data hiding [5,13,17]. Two commonly employed schemes for image prediction are Bmedian edge detection^(MED) and Bgradient adjacent prediction^(GAP), which are briefly discussed as follows [4].…”