Embedding Image Quality Research Articles

Reversible data hiding with dual pixel-value-ordering and minimum prediction error expansion.

Pixel Value Ordering (PVO) holds an impressive property for high fidelity Reversible Data Hiding (RDH). In this paper, we introduce a dual PVO (dPVO) for Prediction Error Expansion (PEE), and thereby develop a new RDH scheme to offer a better rate-distortion performance. Particularly, we propose to embed in two phases: forward and backward. In the forward phase, PVO with classic PEE is applied to every non-overlapping image block of size 1 × 3. In the backward phase, minimum-set and maximum-set of pixels are determined from the pixels predicted in the forward phase. The minimum set only contains the lowest predicted pixels and the maximum set contains the largest predicted pixels of each image block. Proposed dPVO with PEE is then applied to both sets, so that the pixel values of minimum set are increased and that of the maximum set are decreased by a unit value. Thereby, the pixels predicted in the forward embedding can partially be restored to their original values resulting in both a better embedded image quality and a higher embedding rate. Experimental results have recorded a promising rate-distortion performance of our scheme with a significant improvement of embedded image quality at higher embedding rates compared to the popular and state-of-the-art PVO-based RDH schemes.

Reversible data hiding with interpolation and adaptive embedding

Interpolation based reversible data hiding (IRDH) schemes have recently been studied for better rate-distortion performance. However, most of them do not have any consideration of an ‘effective’ capacity management for increasing size of payload. In this paper, we develop and present an IRDH scheme with adaptive embedding, which determines how many bits of an interpolated pixel can be used for the best possible embedded image quality by using a parameter to control the embedding rate. While compared with the prominent IRDH schemes, our scheme demonstrated its efficiency for better embedding rate distortion performance. Being up-sampled, the embedded image would have higher spatial resolution. It also does not require any location map, and thus the total capacity can be effectively used for data embedding. Moreover, it keeps the original pixels untouched and thus, would be useful in military and medical image applications that restrict minimum possible changes in the cover images.

Holographic microinformation hiding.

We propose a holographic microinformation hiding scheme in which the embedding information to be embedded is small and imperceptible to the human eyes. This scheme converts the embedding information into a complex amplitude via scaled diffraction. The complex amplitude of the reduced embedding information is added to the complex amplitude of the host image, followed by conversion to a hologram. The reduced embedded information is inconspicuous from the hologram during the reconstruction process; however, the reduction leads to the degradation of the embedded image quality. Therefore, to improve the quality of the embedded image quality, we employ iterative optimization and the time averaging effect of multiple holograms.

Tamper detection and image recovery for BTC-compressed images

To ensure the integrity of images compressed using block truncation coding (BTC), a tamper detection and image recovery scheme is proposed in this paper. In this scheme, the size of the authentication data can be adaptively selected according to the user’s requirement. The authentication data is embedded in the value differences of the quantization levels in each BTC-compressed image block. Multiple copies of the recovery data are embedded into the bit maps of the smooth blocks. Experimental results show that the proposed scheme performs well in terms of detection precision and the embedded image quality. Meanwhile, the tampered areas can be roughly recovered by using the proposed scheme.

Subsampling-Based Image Tamper Detection and Recovery Using Quick Response Code

Quick response (QR) code is the prevalent trademark for a type of matrix barcode symbol. That code is always scanned to efficiently acquire data, especially for mobile device users. It involves the capabilities of data storage, reliable readability and strong error correction. This paper utilizes these properties of a QR code to propose an image tamper detection and recovery scheme for grayscale images. The image to be protected is first subsampled and decomposed the principal energy compaction for image blocks. These are regarded as the image authentication and recovery data and then are encoded into the pattern of a QR code. The QR code is subsequently embedded into the original image. Experimental results showed that the authentication and recovery data in a QR code is able to sustain certain perceptible distortions such that the proposed scheme can detect tampered regions clearly and recover them roughly. Moreover, the proposed scheme also provides a better embedded image quality in comparison with the previous method.

Block-based reversible data embedding

This paper proposes a block-based reversible data embedding method applied to grayscale cover images. The image is first divided into two areas: data embedding area and auxiliary information embedding area. The data embedding area is further partitioned into 3∗3 blocks. According to two predefined thresholds, all the blocks are divided into three categories: smooth, normal or complex. The complex blocks do not embed any data. A smooth block will embed twice the data of a normal block. After predicting the edge direction in a smooth or normal block, Alattar's difference expansion of a generalized integer transform method is exploited to embed data into each vector in the block. Then, the location map and other auxiliary information are embedded in the auxiliary information embedding area using a novel least significant bit (LSB) substitution method. Our method not only improves the embedded image quality but also provides a higher payload capacity compared to Alattar's method. For some smoother images, the embedding capacity of the proposed method is higher than 1 bit/pixel in a single pass and also derives acceptable embedded image quality (PSNR⩾30). To the best of our knowledge, the proposed method is the first to do this.