## 1 Introduction
(2019) [23] proposed an embedding method based on an AMBTC (absolute moment block truncation coding) compressed image. They used an adaptive variable N-bit bit-plane truncated image embedding method to embed secret data in each block. Here, the secret data are extracted from the receiver side, and the stored peaks and zeros are retrieved to restore the original AMBTC image. Since the histogram of the image is used for data hiding, the amount of hidden data can vary greatly depending on the characteristics of the image. [PERSON] et al. (2022) [24] utilized the redundant space derived from the quantization level to insert a secret message. Data hiding was then performed using a labeling strategy. It achieved a higher embedding capacity than most existing methods. Meanwhile, Block Truncation Coding (BTC) [25] is one of the available compression methods, and the configuration of the BTC is very simple compared to conventional JPEG. Thus, the computation time of BTC is much shorter than that of JPEG, and the quality of an image based on BTC is not significantly deteriorated compared to that of the original image. For this reason, it seems that many researchers are interested in DH based on Absolute Moment BTC (AMBTC) [26], which was developed from BTC recently. [PERSON] and [PERSON] [27] proposed a DH method in which each block of the bitmap is divided into a smooth block and a complex block, and then the bitmap of the smooth block is replaced with a secret bit. A block is divided into a smooth block and a complex block with the difference value (threshold value: \(T\)) between the two quantization levels representing the block, thereby controlling the quality of the image. That is, if the threshold value \(T\) is lowered, the image quality is improved, but the data hiding capacity is reduced. If the size of the threshold value increases, the image quality can be lowered and the hidden data capacity can be increased. [PERSON] and [PERSON] [28] proposed a method by which to adjust image distortion by adjusting two quantization levels, but the original image is required for recomputation. [PERSON] et al. [29] proposed a lossless DH method using two quantization level orders. In this paper, we proposed the SRDH-EI method for AMBTC. AMBTC has the advantage of not having a high computational complexity, which can be useful for real-time video and non-critical image/video processing. In addition, low-resolution compressed images are suitable for efficient use in low-power communication based on WSN (WirelessSensor Networks). The proposed SRDH-EI method consists of the following three steps: image encryption, data hiding, data extraction, and image recovery. AMBTC consists of a bitmap and two quantization levels and is encrypted with a bitmap and a stream cipher with random bits for the two quantization levels. Here, the bit operation, such as XOR, is often used as the encryption/decryption operation. The goal of the proposed model was to achieve the scenario of SRDH-EI. Data hiding uses a Hamming code to hide the message in an encrypted bitmap. Data hiding using the Hamming code has the advantage of good data hiding efficiency and can minimize errors occurring in the cover image. As a result, the image quality of the decrypted image is very good. By conducting simulations, we demonstrated that this method achieves high embedding capacity, good image quality, and error-free hidden bit restoration. The remainder of this paper is structured as follows. Section 2 describes related studies. Section 3 describes and discusses SRDH-EI. Section 4 presents the experimental results. Finally, Section 5 provides the conclusion.