Optimum Histogram Pair Based Image Lossless Data Embedding

Xuan, Guixin; Shi, Yun Q.; Chai, Peiqi; Teng, Jianzhong; Ni, Zhicheng; Tong, Xuefeng

doi:10.1007/978-3-642-01757-5_5

Cited by 31 publications

(62 citation statements)

References 9 publications

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“…Table 1 Considering the GAP is the best predictor of all, we now compare the performance of the ASPE with the GAP predictor against the well known high capacity data hiding schemes reported in the literature, such as [5], [7], [10], [11], [12], [13], and [18]. One of the main features of our scheme is that the quality of marked image is impaired proportional to the required capacity.…”

Section: Experimental Results and Evaluationsmentioning

confidence: 99%

“…Following the methods of LSB, difference-expansion and vector quantization algorithm groups, the last category of data hiding reported in [10], [11], [12], [13] and [14] which have attracted great interests in recent years began by the work of Ni et al [10]. The main idea in this category is to use distribution of numbers.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Lin and Hsueh [12] presented a reversible method based on increasing the differences between two adjacent pixels. Xuan et al [13] , [14] reported a remarkable reversible method where operations are carried out in the integer wavelet transform domain.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Fallahpour

Megías

Ghanbari

2010

Multimed Tools Appl

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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.

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Section: Experimental Results and Evaluationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Fallahpour

Megías

Ghanbari

2010

Multimed Tools Appl

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show abstract

“…Conversely, distortion is caused by both expansion and shifting. In the case of histogram shifting, an optimal approach is proposed by Hwang and others [17] and Xuan and others [20]. In general, probability distribution of the prediction error is Laplacian and exponentially decreases from the center of the histogram.…”

Section: Introductionmentioning

confidence: 99%

Reversible Watermark Using an Accurate Predictor and Sorter Based on Payload Balancing

Kang¹

2012

ETRI J

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A series of reversible watermarking technologies have been proposed to increase embedding capacity and the quality of the watermarked image simultaneously. The major skills include difference expansion, histogram shifting, and optimizing embedding order. In this paper, an accurate predictor is proposed to enhance the difference expansion. An efficient sorter is also suggested to find a more desirable embedding order. The payload is differently distributed into two sub-images, split like a chessboard pattern, for better watermarked image quality. Simulation results of the accurate prediction and sorter based on the payload balancing method yield generally better performance over previous methods. The gap is wide, in particular, in low payload for natural images. The peak signal-to-noise ratio improvement is around 2 dB in low payload ranges.

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“…Recently, Lin and Hsueh [13] suggested a lossless scheme based on increasing the differences between two adjacent pixels. Among the studies performed on transform domain Xuan et al [14] reported the remarkable reversible method carried out in the integer wavelet transform domain.…”

Section: Introductionmentioning

confidence: 99%

Lossless image data embedding in plain areas

2011

Self Cite

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This letter presents a lossless data hiding scheme for digital images which uses an edge detector to locate plain areas for embedding. The proposed method takes advantage of the well-known gradient adjacent prediction utilized in image coding. In the suggested scheme, prediction errors and edge values are first computed and then, excluding the edge pixels, prediction error values are slightly modified through shifting the prediction errors to embed data. The aim of proposed scheme is to decrease the amount of modified pixels to improve transparency by keeping edge pixel values of the image. The experimental results have demonstrated that the proposed method is capable of hiding more secret data than the known techniques at the same PSNR, thus proving that using edge detector to locate plain areas for lossless data embedding can enhance the performance in terms of data embedding rate versus the PSNR of marked images with respect to original image.

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Optimum Histogram Pair Based Image Lossless Data Embedding

Cited by 31 publications

References 9 publications

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

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

Reversible Watermark Using an Accurate Predictor and Sorter Based on Payload Balancing

Lossless image data embedding in plain areas

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