Abstract Adaptive embedding plays an important role in improving the embedding performance of reversible data hiding and it is usually realized by modifying the prediction‐errors discriminately. By extending the skewed histogram shifting technique which uses a pair of extreme predictions to determine whether the target pixel should be predicted or not, this paper realizes another form of adaptive embedding, that is, adaptive prediction‐error generation. Specifically, it is proposed to adaptively determine the pair of extreme predictions according to image content. Each pair of extreme predictions is first evaluated by the introduced distortion per embedding one bit. Then, an efficient mechanism to determine the best pair of extreme predictions for pixels with a given local complexity is designed. With such a mechanism solving the computational problem, it is also proposed to extend the context to obtain more pairs of extreme predictions such that more precise multi‐predictor can be realized. With obtained errors, the best expansion bins are determined to achieve a more comprehensive self‐adaption. Experimental results demonstrate that the proposed scheme achieves better capacity‐distortion performance and outperforms a series of state‐of‐the‐art schemes.

Recent reversible data hiding (RDH) work tends to realize adaptive embedding by discriminately modifying pixels according to image content. However, further optimization and computational complexity remain great challenges. By presenting a better incorporation of pixel value ordering (PVO) prediction and pairwise prediction-error expansion (PEE) technologies, this paper proposes a new RDH scheme. The largest/smallest three pixels of each block are utilized to generate error-pairs. To achieve optimization of the distribution of error pairs, two-layer embedding is introduced such that full-enclosed pixels of each block can be used to determine how to optimally define the spatial location of pixels within block. Then, to modify error pairs with less distortion introduced, the shifted pairing error is involved in the separable utilization of the other one; i.e., it serves as the context for recalculating the other one. Since the recalculation is equivalent to expansion bins selection, various extensions of original pairwise PEE are designed, parameterized, and combined into the so-called multiple pairwise PEE, with which the 2D histogram can be divided into a set of sub-ones for more accurate modification. The experimental results verify the superiority of the proposed scheme over several PVO-based schemes. On the Kodak image database, the average PSNR gains over original PVO-based pairwise PEE are 0.83 and 0.99 dB for capacities of 10,000 and 20,000 bits, respectively.

Pixel value ordering prediction has been verified as an effective mechanism to exploit image redundancy for reversible data hiding (RDH) and numerous extensions have been devised. However, their performance is still unsatisfactory since the error modification is generally fixed and independent of image content. In this paper, a new RDH scheme is proposed by incorporating pixel distance to realize adaptive multiple histograms modification (AMHM). During exploiting the correlation between the largest/smallest pixel and any other one in the scope of pixel block, we propose to process every two correlated pixels successively following the ascending order of their distance. Specifically, the generated errors with a given distance are collected and verified. If they are all shiftable errors, the follow-up errors would be collected into the next sub-histogram. In this way, a histogram sequence is adaptively generated such that different modification mechanisms can be taken for different sub-histograms to achieve adaptive embedding. Finally, AMHM for conventional prediction-error expansion (PEE) and AMHM for 2D PEE have been both realized in this paper. Experimental results show that AMHM is of great significance to better exploit pixel correlation and the proposed scheme outperforms a series of the latest schemes.

JPEG lossy image compression is a still image compression algorithm model that is currently widely used in major network media. However, it is unsatisfactory in the quality of compressed images at low bit rates. The objective of this paper is to improve the quality of compressed images and suppress blocking artifacts by improving the JPEG image compression model at low bit rates. First, the image texture adaptive non-uniform rectangular partition (ITANRP) algorithm is proposed which partitions the image into$8\times 8$size image blocks with high texture complexity and$16\times 16$size image blocks with low texture complexity. Then, a new transform coding based on the complete orthogonal U-system and all-phase digital filter (APDF) is proposed for coding image blocks with different sizes. Next, a flexible adaptive quantization scheme is designed to quantize image blocks with different sizes by considering the sensitivity of the human visual system (HVS) to different texture complexities. Finally, combining the above method with the JPEG model, a novel image compression algorithm model with low algorithm complexity is proposed to solve the problem in JPEG. The experimental results demonstrate that the performance of our algorithm model outperforms the JPEG image compression algorithms, the quality of the compressed image is greatly improved, and the blocking artifacts are also significantly suppressed.

Due to the limitation of existing filters in detection and removal of fixed value impulse noise, the authors propose an iterative grouping median filter (IGMF) according to the characteristics of noise intensity and distribution. It sorts the noise‐free pixels in neighbourhood by intensity, divides the sorted pixels into groups depending on the intensity differences of adjacent pixels, and finally takes the median of the maximum group as the intensity of noisy pixel. This noise removal strategy is performed iteratively and takes full advantage of the previous denoising results. Experiments show that IGMF outperforms the existing state‐of‐the‐art filters in terms of visual perception, peak signal to noise ratio and structural similarity index at various noise densities.