## 2 Related Work  
The existing RDH schemes can be broadly classified into three categories, and they are graphically represented in Figure 3.  
* RDH in original image: The original image will be considered during the RDH process. The quality of the stego image (image obtained after data hiding) is a major concern here. * RDH in the encrypted image: In this, the RDH algorithm will consider the encrypted image. The embedding rate and the security are the two concerns when designing a new RDH scheme in encrypted images. * RDH through encryption: This scheme combines image encryption and data hiding. This section reviews a few well-known RDH schemes introduced for clinical data transmission during medical image transmission. Figure 3: Types of RDH schemes. The existing RDH schemes mainly explored key concepts such as histogram shifting, difference expansion, lossless compression, and image scaling. Of course, many other approaches in the literature utilize entirely different ideas for RDH. The basic version of the histogram-shifting approach is introduced in [25]. The scheme followed a RDH process in a natural image. The histogram of the cover image is computed first, and the grayscale value corresponding to the peak in the histogram is taken as the reference pixel value. Let us assume the peak intensity value is \(P_{K}\), and the approach discussed in [25] increments all the pixel values by one if the grayscale value in the cover image is greater than \(P_{K}\). This process will create a gap in the histogram just after the grayscale value \(P_{K}\). The maximum embedding capacity possible from this approach will be \(C_{K}\), where \(C_{K}\) is the number of pixels with grayscale value \(P_{K}\). As per this scheme, the secret message bits can be embedded in the pixels with grayscale value \(P_{K}\). To embed an extra bit \(B\) in a cover image pixel with grayscale value \(P_{K}\), we need to add the \(B\) value with \(P_{K}\). The embedding capacity of the scheme is purely dependent on the peak in the histogram of the cover image. One of the major concerns of the histogram-shifting RDH approach is overflow or underflow during the histogram shifting. In 2020, an efficient method to handle overflow or underflow in the histogram-shifting RDH scheme was discussed [26]. The system ensures an efficient representation of the location map to distinguish overflow/underflow pixels from original pixels. The method claims that the total length of the location map will be the number of 254 s and 255 s in the cover image. Later, in 2021, a block-wise histogram shifting RDH with overflow handling was discussed in [27]. In this scheme, the histogram is computed from the non-overlapping image blocks of size \(B\times B\) pixels. The block-wise histogram RDH scheme helps to improve the embedding rate. Many other things need to be handled in the block-wise histogram RDH, such as the peak intensity from each block, the blocks used for RDH purposes, etc. The marker information (which is used to distinguish the blocks that are used for RDH from other blocks) is compressed using run-length encoding. Overall results show an excellent, effective embedding rate as compared to the existing approaches, even after embedding the additional overhead details in the image itself. A number of approaches based on prediction error histogram processing were also reported in the literature [28; 29]. In these schemes, the histogram is generated from the prediction error instead of grayscale pixel values from the image. In a prediction error histogram-based RDH, a predefined prediction approach will be used to predict each of the pixels in the original image. Then, the prediction error will be computed to compute the prediction error histogram. The prediction error computation is defined in Equation (1). \[P_{X,Y}^{E}=P_{X,Y}-P_{X,Y}^{P} \tag{1}\]  
where \(P^{E}\) is the prediction error corresponding to the original pixel \(P_{X,Y}\) when \(P_{X,Y}^{P}\) is the predicted pixel value.